Mine object detection based on space attention in coal mine edge intelligent surveillance images

Intelligence is an inevitable trend for the safe production and rapid development of coal mines, and it is an important direction for the development of coal mine intelligence to build the framework of intelligent production system, cloud-edge computing synergy system, and fast and accurate object detection for key targets in coal mines. However, the existing deep learning-based object detection algorithms are large in size and high in computational complexity, making it difficult to deploy them to edge devices to provide low-latency data analysis services. The article proposes a coal mine focused object detection method based on the YOLO-v4L-EA algorithm, which deploys the lightweight YOLO-v4 incorporating the spatial attention mechanism to the edge computing devices of the mine IoT system, so as to provide users with focused object sensing services with high response speed. At the algorithmic level, to address the problem of uneven brightness and other defects in coal mine underground images that affect the effect of object detection, the article designs the pixel regularized spatial attention structure (PNSAM), which implements the spatial attention mechanism with batch regularization, which can assist the target detection model to strengthen the attention to important features and help the algorithm to perceive the task target from low-quality images; inspired by the MobileNet base inspired by the MobileNet base structure, the YOLO-v4 backbone network is lightweight and improved based on the depth-separable convolution, so that the overall model can be deployed in the mine edge computing devices; in order to reduce the gradient loss caused by the h-swish activation function, this paper tries to use the Mish activation function in the model, which achieves efficient deep feature extraction by virtue of its gradient-smoothing property. A mine object detection dataset is constructed based on coal mine video surveillance data for evaluating the practical application performance of the object detection network model. The article uses the NVIDIA Jetson TX2 edge computing platform as the experimental hardware equipment, and the comparison experiments show that the experimental results of the model on the public test dataset VOC2012 improve the mAP value by 13.39% relative to the YOLO-v4-Tiny model, which proves the validity and correctness of the algorithm; the mAP value of the model on the mine object detection dataset is 88.9%, indicating that the method can effectively realize the key object detection in coal mine underground.