Small object in the dark light scene using dual-branch channel and spatial

Object detection is an enabling technology of computer vision for locating instances and categorizing classes of objects in images or videos. It has made enormous strides with significant growing of deep learning research over the last decade. However, there are severe challenges on small object detection in complex scenes as they appear with low resolution and have not enough contrast from the background information. This disturbance may cause missed detection and detection accuracy decline for small objects. We propose a small object detection network in the dark light scene based on improved YOLOv5. The network takes YOLOv5 as the baseline and incorporates a channel and spatial dual-branch backbone module, which enhances the details by fusing the features of the two branches. We also introduce a densely linked feature fusion network before detection layers with receptive field block. The fusion network integrates deep features with shallow ones across different scales. A data augmentation module is used to enhance the brightness and limit the contrast for small object detection in the dark light scene. Experiments are carried out on the Dark Face and darkened Vis Drone dataset. The results show that the evaluation index of the proposed method is better than that of the comparison methods. From the detected images, it is obvious that the undetected frame of the small object in the dark light scene decreases and the detection accuracy improves. All of the results show that our model has better performance than some existing methods for small object detection in the dark light scene.