Research on target detection algorithms in complex road conditions

In complex road environments, traditional algorithms for detecting objects often face issues such as misclassification and omission of distant targets and small objects like pedestrians. To overcome these challenges, a new object detection algorithm called SCK-YOLO has been introduced in this paper. This algorithm is an improvement over the model of YOLOv5s network and includes a tiny object Detection Laye. It replaces the computationally intensive C3 module with a more lightweight C2f module and uses the K-means algorithm for anchor box selection, replacing the original anchor box selection method. Experimental comparisons show that the proposed algorithm performs better on the Kitti dataset compared to the original YOLOv5s algorithm, with an increase of 1.59% in mAP@0.5 and a 3.99 % enhancement in mAP@0.5:0.95.