Underground pedestrian detection model based on Dense-YOLO network

The pedestrian detection is a key technology to realize unmanned mining vehicles. The visibility of images captured in low light environment in coal mine is poor, which greatly affects the effect of pedestrian detection. The existing pedestrian detection methods ignore the influence of underground low light environment on target detection precision, and the detection effect is not ideal. In order to solve this problem, an underground pedestrian detection model based on Dense-YOLO network is proposed. The low light images are decomposed into light image and reflection image, and the light image is enhanced by Gamma transformation, weighted logarithmic transformation and contrast-limited adaptive histogram equalization (CLAHE). The enhanced images are weighted and fused by brightness weight and color weight. The bilateral filtering algorithm is used to process the reflection image to enhance the texture of the image. The enhanced light image and the reflection image processed by bilateral filtering are multiplied point by point to reconstruct the RGB image, and the ROF denoising model is used to denoise the fused image globally to obtain the final enhanced image. The dense module with residual block is added to YOLOv3 to build underground pedestrian detection model based on Dense-YOLO network. The addition of residual block is beneficial to avoid gradient disappearance and gradient explosion in the network training process. The experimental results show that the image visibility and pedestrian detection can be improved effectively by enhancing the low light image. The missed detection rate of Dense-YOLO network for enhanced images is 4.55%, which is 14.91% lower than that of RetinaNet network. The underground pedestrian detection model based on Dense-YOLO network effectively reduces the missed detection rate of pedestrian detection.