Low-Illumination Object Detection Method Based on Dark-YOLO

The images acquired in a complex low-illumination environment have problems such as low brightness, high noise, and loss of detailed information. The general object detection method cannot be used directly to achieve relatively ideal results. In this situation, a low-illumination object detection method-Dark-YOLO is proposed. Firstly, the CSPDarkNet-53 backbone network is used to extract the features of low-illumination image, and the path aggregation enhanced module is proposed to further enhance the ability of feature representation. Then, the pyramid balanced attention module is designed to capture multi-scale features and make use of them effectively to generate more discriminant features with different scales. Finally, the prediction intersection over union (IoU) is used to improve the performance of detection head. The IoU prediction branch predicts the IoU value for each prediction box, which makes the object positioning more accurate. The experimental results on the ExDark dataset show that compared with YOLOv4, the mean average precision (mAP) is improved by 4.10%. Dark-YOLO method can effectively improve the performance of object detection in low-illumination scenes.