The accuracy of road object detection is crucial for ensuring the safe driving of autonomous vehicles. Challenges such as small object missed detection, excessive parameters, low accuracy, and poor robustness are commonly observed in current road object detection models. To address the above problems, a road object detection model named FRCP-YOLO is proposed in the present study, which is developed based on the YOLOv8n. Firstly, to reduce the model’s parameters and complexity, the C2f module in the backbone network is replaced with a lightweight FasterNet Block, which enhancing the speed of image feature extraction; then the proposed R-CA module, which is based on a residual block with the Coordinate Attention (CA) mechanism, is introduced to enhance the model’s focus on objects of interest and improve its feature-learning capability. Secondly, to enhance small object detection performance, a high-resolution branch for feature extraction and a detection head for processing these features are introduced, thereby improving the model’s robustness. Finally, PIoU v2 is selected as the bounding box regression loss function to effectively prevent anchor box enlargement, enhance the ability to focus on anchor boxes, and further improve overall detection accuracy. Based on the KITTI dataset, the comparison experiments between FRCP-YOLO and other mainstream algorithms were carried out, FRCP-YOLO achieves object detection accuracies of 0.924 and 0.667 (in terms of mAP@50 and mAP@50–95) on the test set, representing improvements of 5.0% and 6.6% over the baseline model, while reducing parameters by 4%. Comparative experiments were conducted on the BDD100K dataset of complex road scenes. The detection accuracy of FRCP-YOLO outperforms other mainstream algorithms in challenging scenarios, such as dense traffic, occlusions, and night conditions, which verifies the generalization of FRCP-YOLO, highlighting its reliability and effective object detection capabilities in complex scenarios.
Liu et al. (Fri,) studied this question.