EPLS‐YOLO: A Multi‐Scale Object Detection Method for Complex Traffic Scenarios

Abstract

Aiming at the problems of high missed detection rate and unbalanced feature extraction caused by the coexistence of multi‐scale objects in complex traffic scenarios, this paper proposes an improved YOLO11 detection algorithm (EPLS‐YOLO) for autonomous driving. First, the KITTI dataset is reconstructed and expanded, which is uniformly categorised into six classes (Car, Van, Truck, Pedestrian, Cyclist, and Tram). Data augmentation strategies such as Mosaic and random transformations are further adopted to enhance sample diversity. Second, in the backbone network, the Efficient Multi‐Scale Attention (EMA) is embedded into the C3K2 module to construct the C3K2_EMA module, which strengthens the fine‐grained feature representation of small objects and occluded objects. A P2 high‐resolution detection branch is added to cover the scale of extremely small objects, and a Lightweight Shared Convolution Detection Head (LSCD) is designed to achieve efficient fusion of multi‐scale features from shallow and deep layers. Finally, the SlideLoss function is introduced to dynamically assign sample learning weights based on Intersection over Union (IoU), alleviating the problem of unbalanced training of multi‐scale samples. Experimental results show that the proposed EPLS‐YOLO achieves a precision (P) of 94.5%, recall (R) of 92.0%, and mean Average Precision at IoU = 0.5 (mAP50) of 95.7% on the KITTI dataset, which are 0.7, 3.0 and 1.6 percentage‐points higher than those of the original YOLO11, respectively. Notably, the detection performance for small objects is significantly improved. Moreover, the overall detection performance of EPLS‐YOLO outperforms that of mainstream object detection models such as RT‐DETR, YOLOv8, YOLOv10 and YOLOv12, which can meet the demand for accurate perception of full‐scale objects in autonomous driving.