Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226754
Jun-Yu Shen, Cheng-Kai Lu, Lim Lam Ghai
The problem of floating debris in rivers and oceans is growing. To clean floating objects on the water more effectively, IoT-based unmanned boats were chosen for autonomous cleaning. However, the strong light reflections of riverside objects on the water surface pose challenges for vision-based object detection systems to detect small targets. By modifying the pooling module in Spatial Pyramid Pooling and using the TS-YOLO structure to retain the original spatial pyramid advantage, we improve the accuracy of floating litter for detecting objects on rivers. In the experimental results, our proposed method was tested on Pascal VOC, FLOW, and WIDER FACE, which showed good detection capability on mAP with 2.86%, 1%, and 2.28% improvement over the original YOLOv4.
{"title":"A Novel Deep Convolutional Neural Network Pooling Algorithm for Small floating objects detection","authors":"Jun-Yu Shen, Cheng-Kai Lu, Lim Lam Ghai","doi":"10.1109/ICCE-Taiwan58799.2023.10226754","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226754","url":null,"abstract":"The problem of floating debris in rivers and oceans is growing. To clean floating objects on the water more effectively, IoT-based unmanned boats were chosen for autonomous cleaning. However, the strong light reflections of riverside objects on the water surface pose challenges for vision-based object detection systems to detect small targets. By modifying the pooling module in Spatial Pyramid Pooling and using the TS-YOLO structure to retain the original spatial pyramid advantage, we improve the accuracy of floating litter for detecting objects on rivers. In the experimental results, our proposed method was tested on Pascal VOC, FLOW, and WIDER FACE, which showed good detection capability on mAP with 2.86%, 1%, and 2.28% improvement over the original YOLOv4.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124742714","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226829
Zhijie Hu, Chih-Yung Chang, RuiBing Shen, Shih-Jung Wu, Di Hou
With the development of the economy and the improvement of people’s living standards, more and more people start to pay attention to health and exercise, and the smart bracelet is born. The designed smart bracelet in this paper can collect data such as heart rate, exercise, temperature, humidity, altitude, atmospheric pressure, and light intensity in the current environment. The collected data are processed by the STM32F103C8T6 controller. Then the processed data is displayed in the OLED display module. In addition, the corresponding data can be uploaded to the mobile phone for real-time display through the Bluetooth communication module. The test results show that the system is stable and reliable.
{"title":"Smart bracelet based on the Internet of Things","authors":"Zhijie Hu, Chih-Yung Chang, RuiBing Shen, Shih-Jung Wu, Di Hou","doi":"10.1109/ICCE-Taiwan58799.2023.10226829","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226829","url":null,"abstract":"With the development of the economy and the improvement of people’s living standards, more and more people start to pay attention to health and exercise, and the smart bracelet is born. The designed smart bracelet in this paper can collect data such as heart rate, exercise, temperature, humidity, altitude, atmospheric pressure, and light intensity in the current environment. The collected data are processed by the STM32F103C8T6 controller. Then the processed data is displayed in the OLED display module. In addition, the corresponding data can be uploaded to the mobile phone for real-time display through the Bluetooth communication module. The test results show that the system is stable and reliable.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124849985","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227067
Wei You, Chih-Sheng Huang, Kai-Ming Hu, Tzu-Hsin Liu, Kuan-Ting Lai
In the era of AI booming, object detection is an essential technology for computer vision tasks and widely adopted in autonomous driving. We propose a method to enhance object detection accuracy by adding virtual objects to real scenes through augmented reality, thereby quickly generating a large amount of data to facilitate model training. In addition, Augmented Reality (AR) can create data for rare scenarios in real worlds, such as a car flipping over on the road or a cargo overturned, which can alleviate the long-tail problem of AI models. Furthermore, our tool can generate both 2D and 3D bounding boxes directly. To verify our method, we performed transfer learning on YOLOv7 pre-trained model using 30,766 AR synthesized images of 4 traffic-related classes: Person, Car, Bicycle and Motorcycle. The new detector was evaluated on the COCO dataset. Experiments showed that our method can increase the detector accuracy as well its ability of detecting small objects.
{"title":"Augmented Reality for Real Object Detection","authors":"Wei You, Chih-Sheng Huang, Kai-Ming Hu, Tzu-Hsin Liu, Kuan-Ting Lai","doi":"10.1109/ICCE-Taiwan58799.2023.10227067","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227067","url":null,"abstract":"In the era of AI booming, object detection is an essential technology for computer vision tasks and widely adopted in autonomous driving. We propose a method to enhance object detection accuracy by adding virtual objects to real scenes through augmented reality, thereby quickly generating a large amount of data to facilitate model training. In addition, Augmented Reality (AR) can create data for rare scenarios in real worlds, such as a car flipping over on the road or a cargo overturned, which can alleviate the long-tail problem of AI models. Furthermore, our tool can generate both 2D and 3D bounding boxes directly. To verify our method, we performed transfer learning on YOLOv7 pre-trained model using 30,766 AR synthesized images of 4 traffic-related classes: Person, Car, Bicycle and Motorcycle. The new detector was evaluated on the COCO dataset. Experiments showed that our method can increase the detector accuracy as well its ability of detecting small objects.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934501","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226682
Chun-see Tsao, Po-Chyi Su
Many novel image retargeting algorithms have been proposed to adjust the size of images to suit different display devices while minimizing perceptual distortion. Assessing the quality of retargeted images has become an important task for developing such schemes. In this study, we propose an image retargeting quality assessment method based on the importance of objects in an image. We utilize semantic segmentation to classify pixels and assign them different importance values representing the sensitivity of human eyes to distortion. A visual saliency map is created to better match the subjective perception of humans and is then used in the "Aspect Ratio Similarity" measurement to improve its accuracy. Since human eyes tend to be more sensitive to the information loss in images without prominent foreground objects, we introduce an information loss adjustment strategy for such images. The experimental results demonstrate that the proposed method is effective in evaluating image retargeting algorithms and outperforms existing quality assessment methods.
{"title":"Quality Assessment of Image Retargeting based on Importance of Objects","authors":"Chun-see Tsao, Po-Chyi Su","doi":"10.1109/ICCE-Taiwan58799.2023.10226682","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226682","url":null,"abstract":"Many novel image retargeting algorithms have been proposed to adjust the size of images to suit different display devices while minimizing perceptual distortion. Assessing the quality of retargeted images has become an important task for developing such schemes. In this study, we propose an image retargeting quality assessment method based on the importance of objects in an image. We utilize semantic segmentation to classify pixels and assign them different importance values representing the sensitivity of human eyes to distortion. A visual saliency map is created to better match the subjective perception of humans and is then used in the \"Aspect Ratio Similarity\" measurement to improve its accuracy. Since human eyes tend to be more sensitive to the information loss in images without prominent foreground objects, we introduce an information loss adjustment strategy for such images. The experimental results demonstrate that the proposed method is effective in evaluating image retargeting algorithms and outperforms existing quality assessment methods.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070649","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226620
S. Jusoh, Narmeen Al-Hiyari
Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. The experience could be similar to real life or completely different. Despite the fact that VR has been cited as a fantastic choice for teaching and training, the existing publications do not specifically demonstrate how to construct training application with VR technology. The purpose of this paper is to present our personal experience in designing and implementing First Aid application on Oculus Quest.
{"title":"VR First Aid: Step by Step Development Process*","authors":"S. Jusoh, Narmeen Al-Hiyari","doi":"10.1109/ICCE-Taiwan58799.2023.10226620","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226620","url":null,"abstract":"Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. The experience could be similar to real life or completely different. Despite the fact that VR has been cited as a fantastic choice for teaching and training, the existing publications do not specifically demonstrate how to construct training application with VR technology. The purpose of this paper is to present our personal experience in designing and implementing First Aid application on Oculus Quest.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964792","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226695
Isack Farady, Bhagyashri Khimsuriya, Ruchita Sagathiya, Po-Chiang Lin, Chih-Yang Lin
Anomaly detection is an open and challenging problem that aims to detect anomaly in future samples. In this study, we explore a simple but effective solution that utilizes multi-level feature combination in a student-teacher network to improve the prediction result. Our approach combines low-level, middle-level, and high-level features extracted from ResNet18 to capture a range of features from different layers of the network. Through the use of a student-teacher network, we select the best possible generated features from ResNet18 to enhance the prediction performance. Our results demonstrate that combining features from different levels of the network enhances the model's ability to learn and recognize anomalous patterns, and thus improves the accuracy of anomaly detection. Our proposed student-teacher network with ResNet18 backbone achieves a prediction score of 92.80% and 96.90% for Image AUC and Pixel AUC respectively.
{"title":"Implementing Multi-Level Features in a Student-Teacher Network for Anomaly Detection","authors":"Isack Farady, Bhagyashri Khimsuriya, Ruchita Sagathiya, Po-Chiang Lin, Chih-Yang Lin","doi":"10.1109/ICCE-Taiwan58799.2023.10226695","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226695","url":null,"abstract":"Anomaly detection is an open and challenging problem that aims to detect anomaly in future samples. In this study, we explore a simple but effective solution that utilizes multi-level feature combination in a student-teacher network to improve the prediction result. Our approach combines low-level, middle-level, and high-level features extracted from ResNet18 to capture a range of features from different layers of the network. Through the use of a student-teacher network, we select the best possible generated features from ResNet18 to enhance the prediction performance. Our results demonstrate that combining features from different levels of the network enhances the model's ability to learn and recognize anomalous patterns, and thus improves the accuracy of anomaly detection. Our proposed student-teacher network with ResNet18 backbone achieves a prediction score of 92.80% and 96.90% for Image AUC and Pixel AUC respectively.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123906853","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226707
Toshiro Nunome, Daiki Deguchi
This paper evaluates the QoE of video and audio transmission over a full-duplex wireless LAN with interference traffic through a computer simulation and a subjective experiment. We employ a simulation environment with a pair of audiovisual transmission and reception terminals and a pair of interference traffic transmission and reception ones. We investigate the effect of the transmission rate of interference traffic and communication distance in a wireless channel on the output quality of the video and audio stream at the reception terminal. We perform a subjective experiment with the output timing of video and audio obtained by the simulation.
{"title":"QoE Assessment of Audiovisual Streaming over a Full-Duplex Wireless LAN with Interference Traffic","authors":"Toshiro Nunome, Daiki Deguchi","doi":"10.1109/ICCE-Taiwan58799.2023.10226707","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226707","url":null,"abstract":"This paper evaluates the QoE of video and audio transmission over a full-duplex wireless LAN with interference traffic through a computer simulation and a subjective experiment. We employ a simulation environment with a pair of audiovisual transmission and reception terminals and a pair of interference traffic transmission and reception ones. We investigate the effect of the transmission rate of interference traffic and communication distance in a wireless channel on the output quality of the video and audio stream at the reception terminal. We perform a subjective experiment with the output timing of video and audio obtained by the simulation.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125593709","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10227025
Kazuyuki Kojima, Yiming Li
This paper discusses a method for detecting gas leaks in collapsed buildings in disaster areas using multiple micro-drones. In this paper, we develop our own drone with a mass of less than 100 g equipped with a gas sensor and conduct gas detection experiments in an experimental environment. Computational Fluid Dynamics (CFD) is also used to visualize the air flow around multiple micro-drones. The results of the experiments and numerical simulations will show that leaking gases can be detected using gas sensors mounted on the drones by actively rolling up the gases with the propellers of the drones.
{"title":"Gas Leak Detection in Collapsed Buildings Using Multiple Micro-drones","authors":"Kazuyuki Kojima, Yiming Li","doi":"10.1109/ICCE-Taiwan58799.2023.10227025","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227025","url":null,"abstract":"This paper discusses a method for detecting gas leaks in collapsed buildings in disaster areas using multiple micro-drones. In this paper, we develop our own drone with a mass of less than 100 g equipped with a gas sensor and conduct gas detection experiments in an experimental environment. Computational Fluid Dynamics (CFD) is also used to visualize the air flow around multiple micro-drones. The results of the experiments and numerical simulations will show that leaking gases can be detected using gas sensors mounted on the drones by actively rolling up the gases with the propellers of the drones.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"102 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126218472","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226733
Yi-Xian Cai, Shih-Chieh Chen, Chih-Chiang Wang
Machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection.
{"title":"An Implementation of Feature Selection for Detecting LOIC-based DDoS Attack","authors":"Yi-Xian Cai, Shih-Chieh Chen, Chih-Chiang Wang","doi":"10.1109/ICCE-Taiwan58799.2023.10226733","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226733","url":null,"abstract":"Machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122427021","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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