A small object detection method for coal mine underground scene based on YOLOv7-SE

Although current small object detection methods have improved the detection performance, they are mostly objected at conventional scenarios. In harsh underground environments in coal mines, there are difficulties in extracting small object feature information during the underground small object detection process. In order to solve the problem. a small object detection method for coal mine underground scenes based on YOLOv7-SE has been proposed. Firstly, the simulated annealing (SA) algorithm is integrated with the k-means++clustering algorithm to accurately capture small underground objects by optimizing the estimation of initial anchor box values in the YOLOv7 model. Secondly, a new detection layer is added to the YOLOv7 backbone network to obtain high-resolution feature maps of underground small objects, reducing the interference of a large amount of coal dust on the feature representation of underground small objects. Finally, a dual layer attention mechanism is introduced after the aggregation network module in the backbone network to enhance the feature representation of small underground objects. The experimental results show the following points. ① The loss function of the YOLOv7-SE network model after training is stable around 0.05, indicating that the parameter settings of the YOLOv7-SE network model are reasonable. ② The average precision (AP) of helmet detection based on the YOLOv7-SE network model has improved by 13.86%, 25.3%, 16.13%, 12.71%, 15.53%, 11.59% and 12.20% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively. The self rescue device detection AP based on the YOLOv7-SE network model has improved by 12.37%, 20.16%, 15.22%, 8.35%, 19.42%, 9.64% and 7.38% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively.The frames per second (FPS) of the YOLOv7-SE network model has increased by 42.56, 44.43, 31.74, 39.84, 22.74 and 23.34 frames/s compared to Faster R-CNN, RetinaNe, CenterNet, FCOS, SSD and YOLOv5, respectively, and decreased by 9.36 frames/s compared to YOLOv7. The YOLOv7-SE network model effectively enhances the feature extraction capability of the YOLOv7-SE network model for small underground objects while ensuring detection speed. ③ In the detection of safety helmets and self rescue devices, the YOLOv7-SE network model effectively improves missed and false detection, and improves detection precision.