Lightweight coal miners and manned vehicles detection model based on deep learning and model compression techniques: A case study of coal mines in Guizhou region

Intelligent recognition of coal mine workers and manned vehicles (coal mine pedestrian-vehicles) is an important component of video surveillance systems and a key task in the development of coal mine intelligence. However, the detection scene of coal mine pedestrian-vehicles is complex, and deploying large pedestrian-vehicle detection models on limited computing devices is challenging. Balancing between model detection performance and efficiency poses many challenges. This paper proposes a lightweight coal mine pedestrian detection model based on deep learning and model compression techniques. Taking the coal mine video surveillance dataset in Guizhou region as an example. The model accurately and in real-time completes the task of detecting coal mine pedestrian-vehicles, achieving a balance between model detection performance and efficiency. Specifically, in the network model design phase, a lightweight detection model named FCW-YOLO is proposed based on YOLOv8s as the baseline. Faster-Block and coordinate attention are integrated into the feature extraction module of the network, designing a novel C2f-Faster-CA lightweight architecture to reduce redundant channels of the network while adaptively capturing global key information. Furthermore, the WIOU boundary regression loss function is employed to increase the model's focus on common quality samples, addressing issues such as regression errors caused by imbalanced training samples. In the model compression phase, the proposed FCW-YOLO model undergoes channel-level sparsity through a collaborative pruning algorithm, automatically identifying unimportant channels and reducing them, resulting in the FCWP-YOLO model, achieving secondary lightweight design of the coal mine pedestrian-vehicle detection model. Results on a self-built coal mine pedestrian-vehicle detection dataset show that the proposed model has parameters, computational load, and model size of 2.3 M, 4.0 GFLOPs, and 6.0 MB, respectively, achieving compression ratios of 4.9 times, 4.7 times, and 4.4 times compared to the baseline model. The average detection accuracy is 88.7%, an improvement of 1.1%, with a processing speed of only 5.6ms per image. Compared to various lightweight architectures and advanced detection models, this method demonstrates excellent accuracy, lower computational costs, and better real-time performance, providing a feasible coal mine pedestrian-vehicle detection method for resource-constrained coal mine scenarios, meeting the deployment requirements of coal mine video surveillance and enabling real-time alerts for intelligent inspection of coal mine pedestrian-vehicles.