Complex dark environment-oriented object detection method based on YOLO-AS

For object detection in complex dark environments, the existing methods generally have problems such as low detection accuracy, false detection and missed detection. These problems lead to a lack of key information and incomplete context information, which seriously affects the detection effect. Therefore, this paper proposes an object detection method for complex dark environment based on YOLO-AS. First, the Zero-DCES image enhancement module is designed to improve the image quality of the dark environment via adaptive contrast enhancement. Second, a YOLO-AS detection model is constructed, which integrates ECA_ASPP and the SK attention mechanism. The receptive field is expanded by dilated convolution, and dynamic feature detection is realized by combining channel attention, which effectively enhances the ability of multiscale feature expression. Finally, the model is tested on the ExDark dataset and the LOL dataset, and compared with current mainstream models such as YOLOv5s, YOLOv8n and YOLOv11. Experiments show that the proposed method achieves 78.39% map@50 on the ExDark dataset while maintaining a basically unchanged detection speed, which is 5.78% higher than that of the benchmark model, and significantly improves the detection accuracy in complex dark environments.