The Research on Low-Light Autonomous Driving Object Detection Method

Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing, this paper proposes a YOLO-LKSDS automatic driving detection model. Firstly, the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target; then, on the basis of the YOLOv5 model, the Kmeans++ clustering algorithm is introduced to obtain a suitable anchor frame, and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection. Finally, an improved SEAM (Separated and Enhancement Attention Module) attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes. Compared with the original model, the improved YOLO-LKSDS model achieves a 13.3% improvement in accuracy, a 1.7% improvement in mAP, and 240,000 fewer parameters on the BDD100K dataset. In order to validate the generalization of the improved algorithm, we selected the KITTI dataset for experimentation, which shows that YOLOv5’s accuracy improves by 21.1%, recall by 36.6%, and mAP50 by 29.5%, respectively, on the KITTI dataset. The deployment of this paper’s algorithm is verified by an edge computing platform, where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W, demonstrating high real-time capability and energy efficiency.