Implementation of Personal Safety Equipment Tracking & Detection by DeepSORT & YOLOv8

The purpose of this research is to develop better techniques for detecting the presence of safety gear such as helmets and vests in hazardous jobs like building, mining, and policing. With a fully convolutional neural network and a self-attention mechanism for effective item recognition, YOLOv8, a new object detection model, outperforms its predecessors. Notably, YOLOv8 removes anchors, which reduces box predictions and speeds up post-processing. With an impressive 96.6% mean average precision (mAP) accuracy in our custom dataset, YOLOv8 is useful for monitoring personal protective equipment usage and improving worker safety on construction sites. The DeepSORT algorithm integration adds sophistication, allowing for extensive object-tracking capabilities. This collaborative approach strengthens safety regulations, representing a substantial development in occupational safety in high-risk workplaces.