Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150552
Mohd. Altamash, Shailendra Narayan Singh
Software Testing using GUI is basically containing different testing tools which can be automated or manual testing tool. Lots of tools are already available in the market so why we need to make another. In the current scenario, Software Testing lifecycle (STLC) and Software Development lifecycle (SDLC) is an essential parameter in process of testing. Selenium supports programming languages like Java, C], Ruby, Python, Perl, PHP & JavaScript and QTP has Programming language support only for VB script. As working on an application which can interrupt with any programming language and even application should have batch processing so that multiple test cases can be test at one go. The primary motivation behind this research paper is to lead a relative investigation of a few advanced test automation, tools, for example, Selenium (Open Source Free) and Quick Test Professional (QTP), and to assess and analyze these two automated software testing tools to decide their simplicity of operation, ease of use, region of utilization and effectiveness. To resolve the problem of delayed output make it Robust, faster one with high accuracy having threshold unit.
使用GUI的软件测试基本上包含了不同的测试工具，可以是自动的，也可以是手动的测试工具。市场上已经有很多工具了，所以我们为什么还要再做一个呢?在当前的场景中，软件测试生命周期(STLC)和软件开发生命周期(SDLC)是测试过程中必不可少的参数。Selenium支持Java, C]， Ruby, Python, Perl, PHP和JavaScript等编程语言，QTP仅支持VB脚本。在一个可以被任何编程语言中断的应用程序上工作，甚至应用程序都应该有批处理，这样就可以一次测试多个测试用例。本研究论文背后的主要动机是对一些先进的测试自动化工具进行相关研究，例如Selenium (Open Source Free)和Quick test Professional (QTP)，并对这两个自动化软件测试工具进行评估和分析，以确定它们的操作简单性、易用性、使用范围和有效性。为了解决输出延迟的问题，使其具有鲁棒性，速度更快，精度高的阈值单元。
{"title":"Review on Software Testing using GUI based on QTP","authors":"Mohd. Altamash, Shailendra Narayan Singh","doi":"10.1109/ICDT57929.2023.10150552","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150552","url":null,"abstract":"Software Testing using GUI is basically containing different testing tools which can be automated or manual testing tool. Lots of tools are already available in the market so why we need to make another. In the current scenario, Software Testing lifecycle (STLC) and Software Development lifecycle (SDLC) is an essential parameter in process of testing. Selenium supports programming languages like Java, C], Ruby, Python, Perl, PHP & JavaScript and QTP has Programming language support only for VB script. As working on an application which can interrupt with any programming language and even application should have batch processing so that multiple test cases can be test at one go. The primary motivation behind this research paper is to lead a relative investigation of a few advanced test automation, tools, for example, Selenium (Open Source Free) and Quick Test Professional (QTP), and to assess and analyze these two automated software testing tools to decide their simplicity of operation, ease of use, region of utilization and effectiveness. To resolve the problem of delayed output make it Robust, faster one with high accuracy having threshold unit.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When it comes to diagnosing patients’ illnesses, digital image modalities like X-ray, Ultrasound (US), Computer Tomography (CT), Magnetic resonance imaging (MRI), etc. play an essential part. Noise is a common problem in the pictures produced by these modalities, reducing image quality. An important factor in making correct diagnosis of illness is the quality of the medical pictures used. Poisson noise is a prevalent problem in X-ray pictures. Hairline fractures inside bones, chest coughs, and other similar conditions become more difficult to diagnose when this noise is present. These sounds need to be eliminated from the X-ray picture before it may be improved. In this study, we aimed to establish a method for effectively denoising X-ray pictures, hence reducing the amount of Poisson noise present in them. The suggested filter makes use of the Absolute Difference and Mean Filter (ADMF) to replace the processed pixel with the mean of its nearest neighbors within a 5x5 frame when the absolute difference between them is minimal. Using 75 X-rays of teeth from the Digital Dental X-ray Database, the proposed technique is compared to the state-of-the-art Region Classification and Response Median Filtering (RCRMF) method. Filter performance is measured by Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) scores; the suggested approach improves PSNR by 5.41 percentage points and reduces MSE by 33.44 percentage points.
{"title":"Reduction of Noise in Medical Imaging Quality","authors":"Gandi Vivek Sai, Chekuri Seshank, Pothina Prudhvi Sai Krishna, Jagjit Singh Dhatterwal","doi":"10.1109/ICDT57929.2023.10150846","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150846","url":null,"abstract":"When it comes to diagnosing patients’ illnesses, digital image modalities like X-ray, Ultrasound (US), Computer Tomography (CT), Magnetic resonance imaging (MRI), etc. play an essential part. Noise is a common problem in the pictures produced by these modalities, reducing image quality. An important factor in making correct diagnosis of illness is the quality of the medical pictures used. Poisson noise is a prevalent problem in X-ray pictures. Hairline fractures inside bones, chest coughs, and other similar conditions become more difficult to diagnose when this noise is present. These sounds need to be eliminated from the X-ray picture before it may be improved. In this study, we aimed to establish a method for effectively denoising X-ray pictures, hence reducing the amount of Poisson noise present in them. The suggested filter makes use of the Absolute Difference and Mean Filter (ADMF) to replace the processed pixel with the mean of its nearest neighbors within a 5x5 frame when the absolute difference between them is minimal. Using 75 X-rays of teeth from the Digital Dental X-ray Database, the proposed technique is compared to the state-of-the-art Region Classification and Response Median Filtering (RCRMF) method. Filter performance is measured by Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) scores; the suggested approach improves PSNR by 5.41 percentage points and reduces MSE by 33.44 percentage points.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132874987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150453
Yash Gupta, Frankly Chauhan, Kanika Singla
Numerous asphalt pavement faults are the major contributor to auto accidents, necessitating corrective action because they put people in grave danger. As a result, there are many algorithms used to detect those road damages so that no further accidents occur in the future. A model is proposed which consist of Convolutional Neural Network and ResNet algorithm to find the accuracy in both sections. First, the training dataset is collected from the RDD2020 dataset, which consists of 7000 images of three different countries then labeling of those images, is done in different categories of cracks like longitudinal, alligator, potholes, and traverse cracks. Furthermore, we implement CNN and ResNet architecture to analyze the accuracy and use a better algorithm to detect road damage in the future. After applying the CNN and ResNet-34, 94.79% and 89.94% accuracies are obtained as an outcome.
{"title":"Analysis of Different Deep Learning Algorithms for Road Surface Damage Detection","authors":"Yash Gupta, Frankly Chauhan, Kanika Singla","doi":"10.1109/ICDT57929.2023.10150453","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150453","url":null,"abstract":"Numerous asphalt pavement faults are the major contributor to auto accidents, necessitating corrective action because they put people in grave danger. As a result, there are many algorithms used to detect those road damages so that no further accidents occur in the future. A model is proposed which consist of Convolutional Neural Network and ResNet algorithm to find the accuracy in both sections. First, the training dataset is collected from the RDD2020 dataset, which consists of 7000 images of three different countries then labeling of those images, is done in different categories of cracks like longitudinal, alligator, potholes, and traverse cracks. Furthermore, we implement CNN and ResNet architecture to analyze the accuracy and use a better algorithm to detect road damage in the future. After applying the CNN and ResNet-34, 94.79% and 89.94% accuracies are obtained as an outcome.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151083
Aishwarya Prakash, S. Chauhan
Automated feature learning is now possible in various fields, including healthcare, image recognition, and, more recently, feature extraction and classification of simple and complex human activity detection in mobile and wearable sensors, thanks to advances in deep learning and increased computing capabilities. A significant advancement in artificial intelligence has been made as a result of deep learning and cloud technology integration. As a result of cloud computing, organisations now have access to the necessary resources to develop and implement deep learning solutions. Although it is becoming increasingly common in cloud infrastructures, there is limited research on it. This study aims to provide a comprehensive overview of deep learning and discusses the methodologies, their uniqueness, benefits, and limits. Finally, we define and discuss certain open research difficulties that demand more investigation and improvements.
{"title":"A Comprehensive Survey of Trending Tools and Techniques in Deep Learning","authors":"Aishwarya Prakash, S. Chauhan","doi":"10.1109/ICDT57929.2023.10151083","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151083","url":null,"abstract":"Automated feature learning is now possible in various fields, including healthcare, image recognition, and, more recently, feature extraction and classification of simple and complex human activity detection in mobile and wearable sensors, thanks to advances in deep learning and increased computing capabilities. A significant advancement in artificial intelligence has been made as a result of deep learning and cloud technology integration. As a result of cloud computing, organisations now have access to the necessary resources to develop and implement deep learning solutions. Although it is becoming increasingly common in cloud infrastructures, there is limited research on it. This study aims to provide a comprehensive overview of deep learning and discusses the methodologies, their uniqueness, benefits, and limits. Finally, we define and discuss certain open research difficulties that demand more investigation and improvements.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150988
Kailash Sharma, Nagendra Kumar, Priyanka Datta, Jay Singh, Aditya Verma
Fire Fighting is considered one of the most dangerous Rescue operations that has caused many fire-fighters to lose their life. There were more than a thousand cases where the freighters lost their lives because they were made extra efforts to reach the places Inaccessible to the Human reach. Taking all these problems and the hindrance faced in the past few years we decided to bring technology to its best use and make the most of it. We will introduce you to the paper FROZONE- AUTONOMOUS FIRE FIGHTING ROBOT" which will make use of fire sensors and a controlled water splash to detect fire at the inaccessible places and do the work for the fire-fighters and make their work a little less risky.
灭火被认为是最危险的救援行动之一，导致许多消防员丧生。有一千多个案例中，货轮失去了生命，因为他们在人类无法到达的地方付出了额外的努力。考虑到过去几年面临的所有这些问题和障碍，我们决定将技术发挥到最好，并充分利用它。我们将向您介绍一款名为“FROZONE- AUTONOMOUS FIRE FIGHTING ROBOT”的机器人，该机器人将利用火灾传感器和可控的水花在难以接近的地方探测火灾，并为消防员工作，降低他们的工作风险。
{"title":"A Research Paper on Frozone: An Autonomous Fire fighter","authors":"Kailash Sharma, Nagendra Kumar, Priyanka Datta, Jay Singh, Aditya Verma","doi":"10.1109/ICDT57929.2023.10150988","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150988","url":null,"abstract":"Fire Fighting is considered one of the most dangerous Rescue operations that has caused many fire-fighters to lose their life. There were more than a thousand cases where the freighters lost their lives because they were made extra efforts to reach the places Inaccessible to the Human reach. Taking all these problems and the hindrance faced in the past few years we decided to bring technology to its best use and make the most of it. We will introduce you to the paper FROZONE- AUTONOMOUS FIRE FIGHTING ROBOT\" which will make use of fire sensors and a controlled water splash to detect fire at the inaccessible places and do the work for the fire-fighters and make their work a little less risky.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150547
Brijendra Gupta, P. Sreelatha, M. Shanmathi, John Philip Bhimavarapu, P. John Augustine, K. Sathyarajasekaran
The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the Biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place.
{"title":"A Smart Handling of Bio-Medical Waste and its Segregation with Intelligant Machine Learning Model","authors":"Brijendra Gupta, P. Sreelatha, M. Shanmathi, John Philip Bhimavarapu, P. John Augustine, K. Sathyarajasekaran","doi":"10.1109/ICDT57929.2023.10150547","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150547","url":null,"abstract":"The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the Biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The deaf and dumb community’s primary mode of communication is signs. It is the only source through which deaf and dumb people can communicate with others. The goal of this paper is to invent a model for translating signs into text format. With assistance of machine learning algorithms, we will scan the signs and then convert them to understandable text. KNN (k-nearest neighbour) algorithm will be used to do so. User will get an interface where it can train the system according to their signs and meanings with respect to it, which can later be used for interaction between deaf and dumb people and common people and vice versa. The assessment of this model is conducted with 3 students using various training examples. The accuracy obtained is approximately 97%.
{"title":"An Improved Sign Language Translation approach using KNN in Deep Learning Environment","authors":"Neeraj Kumar Pandey, Aakanchha Dwivedi, Mukul Sharma, Arpit Bansal, A. Mishra","doi":"10.1109/ICDT57929.2023.10150934","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150934","url":null,"abstract":"The deaf and dumb community’s primary mode of communication is signs. It is the only source through which deaf and dumb people can communicate with others. The goal of this paper is to invent a model for translating signs into text format. With assistance of machine learning algorithms, we will scan the signs and then convert them to understandable text. KNN (k-nearest neighbour) algorithm will be used to do so. User will get an interface where it can train the system according to their signs and meanings with respect to it, which can later be used for interaction between deaf and dumb people and common people and vice versa. The assessment of this model is conducted with 3 students using various training examples. The accuracy obtained is approximately 97%.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115313285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image processing is a process to identify the differ- ent patterns in multiple scale. This research explains the funda- mentals of digital image processing and this study provides the significant area of research in image enhancement and deal with multiple parameters on spatial and transform domain. On the other side Image processing which aims to enhance the visibility of input images and extract useful data from them. The most common image transformation is the Fourier transform. There are many applications for the Fourier Transform. Examining photos to identify items and determine their relevance is known as "image processing." A picture analyst examines the distant detected data and makes an effort to find, name, categorise, quantify, and the importance of tangible and cultural items, their through logical processes, patterns and spatial relationships are created.
{"title":"Image Resolution on Multiple Parameters using Spatial and Transform Domain: A Systematic Analysis","authors":"Kapil Joshi, Ajesh F, V. Singh, Sunil Ghildiyal, Prashant Chaudhary, Gunjan Chhabra","doi":"10.1109/ICDT57929.2023.10150817","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150817","url":null,"abstract":"Image processing is a process to identify the differ- ent patterns in multiple scale. This research explains the funda- mentals of digital image processing and this study provides the significant area of research in image enhancement and deal with multiple parameters on spatial and transform domain. On the other side Image processing which aims to enhance the visibility of input images and extract useful data from them. The most common image transformation is the Fourier transform. There are many applications for the Fourier Transform. Examining photos to identify items and determine their relevance is known as \"image processing.\" A picture analyst examines the distant detected data and makes an effort to find, name, categorise, quantify, and the importance of tangible and cultural items, their through logical processes, patterns and spatial relationships are created.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125968277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10150866
L. Maguluri, S. K, M. K, Muhammad Ahtesham Farooqui, H. P. Sultana, S. V.
In general, treatments and medications for diabetics are usually prescribed based on the level of glucose-level in the human-blood. Blood glucose-level was calculated by performing two separate tests: before meals and after meals. The practical functions present in this test enable physicians to carry out appropriate treatment modalities. This paper introduces an improved method of running a machine learning system. Its main task is to accurately analyze the given input data and calculate the correct point of blood-glucose in the blood of diabetics. It was designed to do so and then list the appropriate control methods and drugs and share that data with the user in a paperless digital manner. Thus it is enough for patients to go to laboratories and do tests. Only their results will be calculated and sent to them for treatment.
{"title":"An Artificial Intelligence based Machine Learning Approach for Automatic Blood Glucose Level Identification of Diabetes Patients","authors":"L. Maguluri, S. K, M. K, Muhammad Ahtesham Farooqui, H. P. Sultana, S. V.","doi":"10.1109/ICDT57929.2023.10150866","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150866","url":null,"abstract":"In general, treatments and medications for diabetics are usually prescribed based on the level of glucose-level in the human-blood. Blood glucose-level was calculated by performing two separate tests: before meals and after meals. The practical functions present in this test enable physicians to carry out appropriate treatment modalities. This paper introduces an improved method of running a machine learning system. Its main task is to accurately analyze the given input data and calculate the correct point of blood-glucose in the blood of diabetics. It was designed to do so and then list the appropriate control methods and drugs and share that data with the user in a paperless digital manner. Thus it is enough for patients to go to laboratories and do tests. Only their results will be calculated and sent to them for treatment.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126100751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-11DOI: 10.1109/ICDT57929.2023.10151137
R. Chaturvedi, Udayan Ghose
Object detection had gained importance in previous decade due to large amount of data that is being generated throughout the world by cameras, mobile phones, satellite imaginary, medical image, social media, UAV etc. As hardware cost to render these images had been reduced significantly and we have access to plethora of algorithms, framework to detect the object and use this information to solve day to day problems. The object detection is most researched area but it still fails to detect and recognize small objects as detecting large objects had got more focus. But small object detection had got less attention and the algorithms and methodology developed for detecting large object does not yield the desired results and accuracy. In this paper we attempt to detect small objects by using state of art algorithm yolov7 and roboflow and try to evaluate the robustness of object detection with scarcity of data in dataset.
{"title":"Evaluation of Small Object Detection in Scarcity of Data in the Dataset Using Yolov7","authors":"R. Chaturvedi, Udayan Ghose","doi":"10.1109/ICDT57929.2023.10151137","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151137","url":null,"abstract":"Object detection had gained importance in previous decade due to large amount of data that is being generated throughout the world by cameras, mobile phones, satellite imaginary, medical image, social media, UAV etc. As hardware cost to render these images had been reduced significantly and we have access to plethora of algorithms, framework to detect the object and use this information to solve day to day problems. The object detection is most researched area but it still fails to detect and recognize small objects as detecting large objects had got more focus. But small object detection had got less attention and the algorithms and methodology developed for detecting large object does not yield the desired results and accuracy. In this paper we attempt to detect small objects by using state of art algorithm yolov7 and roboflow and try to evaluate the robustness of object detection with scarcity of data in dataset.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121615895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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