High-accuracy detection method for miner action recognition with shuffle attention SlowFast networks

One of the principal causes of mining accidents is the unsafe conduct of miners. The monitoring of miners’ unsafe actions and the timely implementation of corrective measures can effectively prevent a considerable number of preventable accidents. The SlowFast network represents a representative network for video action recognition. In comparison with conventional two-stream networks, the SlowFast networks avoid the extraction of optical flow information. Nevertheless, the quality of the imaging of coal mine images is inferior to that of regular images, exhibiting deficiencies such as inadequate illumination and the loss of fine details. Furthermore, the original network was incapable of effectively integrating horizontal information, resulting in a further reduction in accuracy when testing for coal miners. To address these issues, a shuffle attention SlowFast network for video action recognition has been proposed. A lightweight yet efficient shuffle attention module is employed to accurately focus on all the relevant elements of the lateral connections while also focusing action information through the attention mechanism. To address the issue of data imbalance, a linear combination of polynomial functions, termed PolyLoss, is employed. The proposed architecture was evaluated on three datasets: the public UCF-101 dataset, the public HMDB-51 dataset, and a self-constructed dataset of miner actions. The experimental results demonstrated that the proposed method exhibited superior performance compared with existing state-of-the-art methods for both regular images and images of underground mines.