WGS-YOLO: A real-time object detector based on YOLO framework for autonomous driving

Abstract

The safety and reliability of autonomous driving depends on the precision and efficiency of object detection systems. In this paper, a refined adaptation of the YOLO architecture (WGS-YOLO) is developed to improve the detection of pedestrians and vehicles. Specifically, its information fusion is enhanced by incorporating the Weighted Efficient Layer Aggregation Network (W-ELAN) module, an innovative dynamic weighted feature fusion module using channel shuffling. Meanwhile, the computational demands and parameters of the proposed WGS-YOLO are significantly reduced by employing the Space-to-Depth Convolution (SPD-Conv) and the Grouped Spatial Pyramid Pooling (GSPP) modules that have been strategically designed. The performance of our model is evaluated with the BDD100k and DAIR-V2X-V datasets. In terms of mean Average Precision (${\text{mAP}}_{0.5}$), the proposed model outperforms the baseline Yolov7 by 12%. Furthermore, extensive experiments are conducted to verify our analysis and the model’s robustness across diverse scenarios.