Lightweight object detection algorithm for road scenes based on YOLOV8

Object detection in road scenes is a crucial component of autonomous driving. Due to the significant variations in target scales, it is prone to false positives, false negatives, and some underperforming devices cannot deploy the state-of-the-art detectors. To address these issues, we propose a lightweight algorithm based on an improved YOLOv8. We simplify the model by using the FasterNet Block from FasterNet to replace the BottleNeck module in YOLOv8's backbone network C2f, reducing parameters and floating-point computations. We also enhance feature extraction by substituting RFCAConv for the downsampling standard convolution in C2f. Additionally, we introduce Wise-IoU to replace the original activation function, directing the network's focus towards anchor boxes of average quality.To promote effective fusion of original, shallow, and deep features, we introduce the BiFPN structure to replace YOLOv8's PAN structure. Furthermore, a small object detection layer is added to the head to handle the drastic scale variations in road scenes. Experimental results on the SODA10M dataset demonstrate that the improved YOLOv8 model achieves a 55.8% mAP@0.5 and a 35.5% mAP@0.5:0.95. The model's parameter count, size, and floating-point computations decrease by 58.3%, 57.0%, and 21.1%, respectively. Analysis of the experimental results confirms that the proposed model is effective and superior, striking a balance between detection accuracy and model lightweightness.