Workplace accidents continue to pose significant human safety risks,particularly in the construction and manufacturing industries.The necessity for effective Personal Protective Equipment(PPE)compliance has become inc...Workplace accidents continue to pose significant human safety risks,particularly in the construction and manufacturing industries.The necessity for effective Personal Protective Equipment(PPE)compliance has become increasingly paramount.We focus on developing non-invasive techniques based on the Object Detection(OD)and Convolutional Neural Network(CNN).The aim is to detect and verify the proper use of various types of PPE such as helmets,safety glasses,masks,and protective clothing.This study proposes the SH17 Dataset,consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments,to train and validate the OD models.We have trained state-of-the-art OD models for benchmarking,and initial results demonstrate promising accuracy levels with You Only Look Once(YOLO)v9-e model variant exceeding 70.9%in PPE detection.The validation of the model across cross-domain datasets indicates that integrating these technologies can substantially enhance safety management systems.This approach offers a scalable and efficient solution for industries seeking to comply with human safety regulations while safeguarding their workforce.The dataset is available at https://github.com/ahmadmughees/sh17dataset.展开更多
Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density est...Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density estimator （G-KDE） that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal- spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content （NIC） is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.展开更多
文摘Workplace accidents continue to pose significant human safety risks,particularly in the construction and manufacturing industries.The necessity for effective Personal Protective Equipment(PPE)compliance has become increasingly paramount.We focus on developing non-invasive techniques based on the Object Detection(OD)and Convolutional Neural Network(CNN).The aim is to detect and verify the proper use of various types of PPE such as helmets,safety glasses,masks,and protective clothing.This study proposes the SH17 Dataset,consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments,to train and validate the OD models.We have trained state-of-the-art OD models for benchmarking,and initial results demonstrate promising accuracy levels with You Only Look Once(YOLO)v9-e model variant exceeding 70.9%in PPE detection.The validation of the model across cross-domain datasets indicates that integrating these technologies can substantially enhance safety management systems.This approach offers a scalable and efficient solution for industries seeking to comply with human safety regulations while safeguarding their workforce.The dataset is available at https://github.com/ahmadmughees/sh17dataset.
文摘Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density estimator （G-KDE） that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal- spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content （NIC） is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.
