RTM-CMD: Exploring Advanced Underground Target Detection in Coal Mines through Modified RTMDET Methodology

Longlong Gao, Tao Xue, Long Xi

Abstract: In the process of coal mine production, coal mine underground video surveillance is of great significance for enhancing coal mine safety production by analyzing and predicting sudden abnormalities in the coal mine production process in advance. However, it is difficult to ensure the safety of personnel by relying solely on human labor, and along with a large number of deep learning network models being proposed in recent years, it has become a challenge to apply target detection to actual mineral production environments. In this paper, we propose an improved RTMDet-Based target detection algorithm for underground coal mine to solve the problems of lack of real-time performance of video surveillance and restricted edge devices that are not easy to be deployed by combining knowledge distillation with target detection model. In order to verify the effectiveness of the method, the improved RTM-CMD model is compared with the RTMDet model and several mainstream target detection models in a comparison experiment. The experimental results show that the improved RTM-CMD model does not cause any significant loss of accuracy compared to the benchmark model in a resource-constrained environment, while still ensuring high accuracy compared to the rest of the models.