Research on mine moving target detection method based on deep learning

In response to the problem of low accuracy in detecting moving targets in minefield images due to indistinct target features, complex background information, and frequent occlusions, this paper proposes a deep learning-based method for minefield moving target detection. Firstly, a fully dynamic convolutional structure is incorporated into the convolutional block of the backbone feature extraction network to reduce redundant information and enhance feature extraction capability. Secondly, the Swin Transformer network structure is introduced during the feature fusion process to enhance the perception of local geometric information. Finally, a coordinate attention mechanism is added to update the fused feature maps, thus enhancing the network's ability to detect occluded targets and targets in low-light conditions. The proposed algorithm is evaluated on a self-built minefield dataset and the Pascal VOC dataset through ablation experiments, and the results show that it significantly improves the average accuracy of target detection in minefield images.