Sihang Zhang, Xiaojun Dou, Zhi Yang, Chang Liu, Bin Zhao, Te Li, Shaohua Wang, Xiao Tan, Gang Qiu
With the construction of city and the continuous expansion of power grid assets, transmission lines are increasingly radiating and expanding from urban areas to suburbs, mountainous areas, and even unmanned areas. Overhead transmission lines exposed in the wild are often susceptible to the impact of tree barriers. When the trees around the transmission line grow to a certain height, causing the distance between the wires and the trees to be too small, it can cause the wires to discharge from the trees, leading to accidents such as short circuits and trips. Therefore, the investigation of tree barriers is a highly concerned issue for various provincial companies. Based on the advantages of satellite remote sensing, such as wide coverage and unrestricted environmental conditions, this article proposes a three-dimensional monitoring method for tree obstacle based on satellites remote sensing images that integrates tower features. The effectiveness of the proposed method in this paper is verified by conducting experiments on a 500 kV transmission line in Chongqing and comparing it with unmanned aerial vehicle monitoring methods.
{"title":"Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features","authors":"Sihang Zhang, Xiaojun Dou, Zhi Yang, Chang Liu, Bin Zhao, Te Li, Shaohua Wang, Xiao Tan, Gang Qiu","doi":"10.1117/12.3014411","DOIUrl":"https://doi.org/10.1117/12.3014411","url":null,"abstract":"With the construction of city and the continuous expansion of power grid assets, transmission lines are increasingly radiating and expanding from urban areas to suburbs, mountainous areas, and even unmanned areas. Overhead transmission lines exposed in the wild are often susceptible to the impact of tree barriers. When the trees around the transmission line grow to a certain height, causing the distance between the wires and the trees to be too small, it can cause the wires to discharge from the trees, leading to accidents such as short circuits and trips. Therefore, the investigation of tree barriers is a highly concerned issue for various provincial companies. Based on the advantages of satellite remote sensing, such as wide coverage and unrestricted environmental conditions, this article proposes a three-dimensional monitoring method for tree obstacle based on satellites remote sensing images that integrates tower features. The effectiveness of the proposed method in this paper is verified by conducting experiments on a 500 kV transmission line in Chongqing and comparing it with unmanned aerial vehicle monitoring methods.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511346","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}
Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains.
{"title":"Airlight estimation in underwater image restoration","authors":"Jinlei Chu, Zhanying Zhang, Dongsheng Yu, Weikai Fang, Yi Cai, Chidong Xu","doi":"10.1117/12.3014363","DOIUrl":"https://doi.org/10.1117/12.3014363","url":null,"abstract":"Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511401","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}
Aiming at the target distribution problem of anti-aircraft weapon firepower, a target allocation method based on multitarget particle swarm is proposed. The mathematical model of incoming target allocation constraint optimization is established, and the multi-target particle swarm algorithm is used to solve the target allocation model. The inertia weights in the velocity update formula of the particle swarm algorithm and the learning factor assignment method are improved. Compared with the simulation results and actual experience judgment, the designed algorithm solves the target problem in the air defense weapon system to a certain extent.
{"title":"Target allocation method based on multi-objective particle swarm optimization algorithm","authors":"Qing Liu, Yunzheng Liu, Dexian Zeng","doi":"10.1117/12.3014410","DOIUrl":"https://doi.org/10.1117/12.3014410","url":null,"abstract":"Aiming at the target distribution problem of anti-aircraft weapon firepower, a target allocation method based on multitarget particle swarm is proposed. The mathematical model of incoming target allocation constraint optimization is established, and the multi-target particle swarm algorithm is used to solve the target allocation model. The inertia weights in the velocity update formula of the particle swarm algorithm and the learning factor assignment method are improved. Compared with the simulation results and actual experience judgment, the designed algorithm solves the target problem in the air defense weapon system to a certain extent.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511728","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}
Face recognition technology is one of the popular research directions in computer vision in recent years, which is widely used in our daily life. Therefore, this paper takes the Yolov5 algorithm as the core, introduces the COCO dataset, and at the same time introduces the Yolov5 system structure and analyzes the algorithm in terms of implementation and performance. Experiments are conducted on the detection of two targets with different genders, and by changing three different hyperparameters (number of training rounds, batch size and image size), we observe the influence of the change of different hyperparameters on the experimental effect and derive the suitable size of different hyperparameters
{"title":"Face detection based on Yolov5","authors":"Jiahang Liu","doi":"10.1117/12.3014517","DOIUrl":"https://doi.org/10.1117/12.3014517","url":null,"abstract":"Face recognition technology is one of the popular research directions in computer vision in recent years, which is widely used in our daily life. Therefore, this paper takes the Yolov5 algorithm as the core, introduces the COCO dataset, and at the same time introduces the Yolov5 system structure and analyzes the algorithm in terms of implementation and performance. Experiments are conducted on the detection of two targets with different genders, and by changing three different hyperparameters (number of training rounds, batch size and image size), we observe the influence of the change of different hyperparameters on the experimental effect and derive the suitable size of different hyperparameters","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511746","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}
Ruohan Zheng, Jianming Miao, Haosu Zhang, Xinyu Liu, Dongxu Tan
In underwater imagery, issues such as non-uniform illumination, blurriness, and low contrast are prevalent, significantly impacting the quality of captured images. In recent years, numerous researchers have delved into underwater image processing. Due to the intricacies of underwater environments, low-light images have different requirements compared to well-illuminated ones. However, existing algorithms often struggle to address the non-uniform illumination issues stemming from various lighting conditions in underwater settings. They also lack the capability to adaptively enhance underwater images with varying brightness. To tackle these challenges, we propose an adaptive illumination enhancement method for underwater images. This algorithm offers the capability to adaptively enhance underwater images suffering from detail blurriness based on their original brightness. Furthermore, it dynamically adjusts the parameters of the gamma function using the image's illumination component to augment color contrast. Experimental results demonstrate that our approach outperforms other algorithms, as evidenced by superior scores in UIQM metric. It effectively addresses edge blurriness and non-uniform illumination issues prevalent in underwater images captured under varying lighting conditions.
{"title":"An illumination adaptive underwater image enhancement method","authors":"Ruohan Zheng, Jianming Miao, Haosu Zhang, Xinyu Liu, Dongxu Tan","doi":"10.1117/12.3014373","DOIUrl":"https://doi.org/10.1117/12.3014373","url":null,"abstract":"In underwater imagery, issues such as non-uniform illumination, blurriness, and low contrast are prevalent, significantly impacting the quality of captured images. In recent years, numerous researchers have delved into underwater image processing. Due to the intricacies of underwater environments, low-light images have different requirements compared to well-illuminated ones. However, existing algorithms often struggle to address the non-uniform illumination issues stemming from various lighting conditions in underwater settings. They also lack the capability to adaptively enhance underwater images with varying brightness. To tackle these challenges, we propose an adaptive illumination enhancement method for underwater images. This algorithm offers the capability to adaptively enhance underwater images suffering from detail blurriness based on their original brightness. Furthermore, it dynamically adjusts the parameters of the gamma function using the image's illumination component to augment color contrast. Experimental results demonstrate that our approach outperforms other algorithms, as evidenced by superior scores in UIQM metric. It effectively addresses edge blurriness and non-uniform illumination issues prevalent in underwater images captured under varying lighting conditions.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511536","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}
Reversible Data Hiding in Encrypted Images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity.
{"title":"Reversible data hiding in encrypted image based on adaptive difference prediction and block subdivision","authors":"Xuesheng Zhang, Jing Wang","doi":"10.1117/12.3014480","DOIUrl":"https://doi.org/10.1117/12.3014480","url":null,"abstract":"Reversible Data Hiding in Encrypted Images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511538","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}
Henan Province is an important agricultural province in China, and its food production is crucial for meeting the country's food needs and ensuring food security. This article establishes a prediction model for grain yield in Henan Province based on BP neural network. Six indicators are selected as input variables, including total power of agricultural machinery, effective irrigation area, converted amount of agricultural fertilizer application, pesticide usage, sowing area of grain crops, and rural electricity consumption. Grain yield is used as output variable. The experimental results show that the error rate of the BP neural network prediction model in the training and validation stages is controlled within 3%, indicating that the model has good prediction performance and is helpful for the government to formulate agricultural planning and agricultural production management strategies.
河南省是中国重要的农业大省，其粮食生产对满足国家粮食需求和确保粮食安全至关重要。本文建立了基于 BP 神经网络的河南省粮食产量预测模型。输入变量包括农业机械总动力、有效灌溉面积、农用化肥折算施用量、农药使用量、粮食作物播种面积和农村用电量。粮食产量作为输出变量。实验结果表明，BP 神经网络预测模型在训练和验证阶段的误差率均控制在 3%以内，表明该模型具有良好的预测性能，有助于政府制定农业规划和农业生产管理策略。
{"title":"A prediction model for grain yield in Henan province based on BP neural network","authors":"Jun Xu, Yaru Yuan","doi":"10.1117/12.3014503","DOIUrl":"https://doi.org/10.1117/12.3014503","url":null,"abstract":"Henan Province is an important agricultural province in China, and its food production is crucial for meeting the country's food needs and ensuring food security. This article establishes a prediction model for grain yield in Henan Province based on BP neural network. Six indicators are selected as input variables, including total power of agricultural machinery, effective irrigation area, converted amount of agricultural fertilizer application, pesticide usage, sowing area of grain crops, and rural electricity consumption. Grain yield is used as output variable. The experimental results show that the error rate of the BP neural network prediction model in the training and validation stages is controlled within 3%, indicating that the model has good prediction performance and is helpful for the government to formulate agricultural planning and agricultural production management strategies.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511348","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}
In view of the difficulties of volume measurement in the evaluation of refrigerator energy efficiency grade, based on the analysis of the principle of each measurement method, combined with the characteristics of refrigerator volume measurement, the technology combining structured light and binocular vision measurement was used. The structured light adopts speckle structured light. After the scanner is calibrated, the data is collected. After the data is denoised, smoothed, and edges are extracted, the volume is calculated. The results show that the structured light 3D scanning measurement accuracy meets the measurement requirements, but there are also problems such as insufficient depth of field measurement of the scanner at the turning point of the refrigerator, and the need to paste a large number of landmarks in the area without obvious features to achieve the stitching of the measured images.
{"title":"Application of binocular structured light 3D measurement technology in refrigerator volume measurement","authors":"Pengli Cheng","doi":"10.1117/12.3014414","DOIUrl":"https://doi.org/10.1117/12.3014414","url":null,"abstract":"In view of the difficulties of volume measurement in the evaluation of refrigerator energy efficiency grade, based on the analysis of the principle of each measurement method, combined with the characteristics of refrigerator volume measurement, the technology combining structured light and binocular vision measurement was used. The structured light adopts speckle structured light. After the scanner is calibrated, the data is collected. After the data is denoised, smoothed, and edges are extracted, the volume is calculated. The results show that the structured light 3D scanning measurement accuracy meets the measurement requirements, but there are also problems such as insufficient depth of field measurement of the scanner at the turning point of the refrigerator, and the need to paste a large number of landmarks in the area without obvious features to achieve the stitching of the measured images.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511776","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 application of autonomous mobile robots is becoming more and more extensive. Path planning is one of the core problems, and the advantages and disadvantages of path planning algorithms directly affect the movement performance of robots. This paper aims to compare the path planning performance of autonomous mobile robots based on the ROS platform using multiple algorithms such as A*, Dijkstra, RRT, PRM, including path length, execution time and stability of robot posture through experiments. The results show that the PRM algorithm generally performs well in planning efficient and stable robot paths
{"title":"Comparative research on path planning algorithms for autonomous mobile robots based on ROS","authors":"Siyu Wang","doi":"10.1117/12.3014664","DOIUrl":"https://doi.org/10.1117/12.3014664","url":null,"abstract":"The application of autonomous mobile robots is becoming more and more extensive. Path planning is one of the core problems, and the advantages and disadvantages of path planning algorithms directly affect the movement performance of robots. This paper aims to compare the path planning performance of autonomous mobile robots based on the ROS platform using multiple algorithms such as A*, Dijkstra, RRT, PRM, including path length, execution time and stability of robot posture through experiments. The results show that the PRM algorithm generally performs well in planning efficient and stable robot paths","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511945","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}
dongmei Liu, Binfeng D. Lin, Yongfeng Li, V. Tarelnyk
In order to solve the disassembly plan of the target parts in the product with high efficiency, a disassembly hybrid graph model of the target parts is proposed and established based on the disassembly connection relationship and disassembly priority constraint relationship between the parts in the product. The disassembly sequence planning problem of the target parts is transformed into a search and optimization problem for the path with the optimal value in the graph model. At the same time, the sorting algorithm is used to solve the mixed graph model of the target part disassembly, finally, an example is given the feasibility of this method has been verified.
{"title":"Research on selective disassembly sequence planning based on graph model","authors":"dongmei Liu, Binfeng D. Lin, Yongfeng Li, V. Tarelnyk","doi":"10.1117/12.3014520","DOIUrl":"https://doi.org/10.1117/12.3014520","url":null,"abstract":"In order to solve the disassembly plan of the target parts in the product with high efficiency, a disassembly hybrid graph model of the target parts is proposed and established based on the disassembly connection relationship and disassembly priority constraint relationship between the parts in the product. The disassembly sequence planning problem of the target parts is transformed into a search and optimization problem for the path with the optimal value in the graph model. At the same time, the sorting algorithm is used to solve the mixed graph model of the target part disassembly, finally, an example is given the feasibility of this method has been verified.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511688","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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