An effective deep learning approach enabling miners’ protective equipment detection and tracking using improved YOLOv7 architecture

In the complex underground mining environment, ensuring the correct wearing of personal protective equipment (PPE) is crucial for coal mine safety production. To overcome the limitations of existing PPE detection and tracking technologies, which often suffer from low precision, slow performance, and complex feature extraction processes, this paper introduces an enhanced, lightweight, and high-precision object detection network model based on YOLOv7. The proposed model incorporates a streamlined backbone feature extraction architecture that combines the Mobile Inverted Bottleneck Convolution module with the GhostBottleneck Lightweight module. This integration significantly improves the detection accuracy of miners’ PPE while simultaneously reducing the number of network parameters. Furthermore, the model adopts adaptive spatial feature fusion to enhance its capability in effectively integrating cross-scale features, thereby further boosting its detection performance. To enable continuous and stable tracking of miners’ PPE usage, this paper integrates the DeepSort tracking algorithm, which is based on OSNet, with the improved YOLOv7 detection model. This combination constructs an efficient video-based multi-object tracking algorithm, providing essential support for enhancing the tracking performance of coal miners’ PPE. Experimental results demonstrate that, compared to other state-of-the-art methods, the proposed model achieves a 2.25% increase in mean Average Precision (mAP), a 2.91% improvement in F1 score, a 0.41% enhancement in precision, and a 5.34% increase in recall for PPE detection. Additionally, it exhibits significant improvements in multi-object tracking metrics, with a 5.9% increase in Multi-Object Tracking Accuracy (MOTA), a 3.5% increase in Multi-Object Tracking Precision (MOTP), and a 6.2% increase in IDF1 score. These results fully validate the model’s efficient detection and tracking capabilities for miners’ PPE in complex underground mining environments.