Multi object detection of underground unmanned electric locomotives in coal mines based on SD-YOLOv5s-4L

Due to complex environmental factors such as uneven illumination and high noise, unmanned electric locomotives in coal mines have low accuracy in multi object detection and difficulty in recognizing small objects. In order to solve the above problems, a multi object detection model for underground unmanned electric locomotives in coal mines based on SD-YOLOv5s-4L is proposed. On the basis of YOLOv5s, the following improvements are made to construct the SD-YOLOv5s-4L network model. The model introduces the SIoU loss function to solve the problem of mismatch between the direction of the real box and the predicted box, so that the model can better learn the position information of the object. The model introduces decoupled heads at the head of YOLOv5s to enhance the feature fusion and positioning accuracy of the network model. It enables the model to quickly capture multi-scale features of the object. The model introduces a small object detection layer to increase the original three scale detection layer to four scale. It enhances the model's feature extraction capability and detection precision for small objects. The experiment is conducted on a multi object detection dataset of the mine electric locomotives. The results show the following points. The mean average precision (mAP) of the SD-YOLOv5s-4L network model for various types of objects is 97.9%, and the average precision (AP) for small objects is 98.9%. Compared with the YOLOv5s network model, it improves by 5.2% and 9.8%, respectively. Compared with other network models such as YOLOv7 and YOLOv8, the SD-YOLOv5s-4L network model has the best comprehensive detection performance and can provide technical support for achieving unmanned driving of the mine electric locomotives.