The Experts below are selected from a list of 31467 Experts worldwide ranked by ideXlab platform
Svetlana Yanushkevich - One of the best experts on this subject based on the ideXlab platform.
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instance segmentation of Personal Protective Equipment using a multi stage transfer learning process
Systems Man and Cybernetics, 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
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SMC - Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
Thomas Truong - One of the best experts on this subject based on the ideXlab platform.
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instance segmentation of Personal Protective Equipment using a multi stage transfer learning process
Systems Man and Cybernetics, 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
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SMC - Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
Aakash Bhatt - One of the best experts on this subject based on the ideXlab platform.
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instance segmentation of Personal Protective Equipment using a multi stage transfer learning process
Systems Man and Cybernetics, 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
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SMC - Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
Leonardo Queiroz - One of the best experts on this subject based on the ideXlab platform.
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instance segmentation of Personal Protective Equipment using a multi stage transfer learning process
Systems Man and Cybernetics, 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
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SMC - Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
Kenneth Lai - One of the best experts on this subject based on the ideXlab platform.
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instance segmentation of Personal Protective Equipment using a multi stage transfer learning process
Systems Man and Cybernetics, 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
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SMC - Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Thomas Truong, Aakash Bhatt, Leonardo Queiroz, Kenneth Lai, Svetlana YanushkevichAbstract:This paper focuses on the instance segmentation of soft attributes on humans such as clothing and Personal Protective Equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment Personal Protective Equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP 50 , of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper Personal Protective Equipment usage while maintaining worker anonymity.
