High-precision real-time autonomous driving target detection based on YOLOv8

Abstract

In traffic scenarios, the size of targets varies significantly, and there is a limitation on computing power. This poses a significant challenge for algorithms to detect traffic targets accurately. This paper proposes a new traffic target detection method that balances accuracy and real-time performance—Deep and Filtered You Only Look Once (DF-YOLO). In response to the challenges posed by significant differences in target scales within complex scenes, we designed the Deep and Filtered Path Aggregation Network (DF-PAN). This module effectively fuses multi-scale features, enhancing the model's capability to detect multi-scale targets accurately. In response to the challenge posed by limited computational resources, we design a parameter-sharing detection head (PSD) and use Faster Neural Network (FasterNet) as the backbone network. PSD reduces computational load by parameter sharing and allows for feature extraction capability sharing across different positions. FasterNet enhances memory access efficiency, thereby maximizing computational resource utilization. The experimental results on the KITTI dataset show that our method achieves satisfactory balances between real-time and precision and reaches 90.9% mean average precision(mAP) with 77 frames/s, and the number of parameters is reduced by 28.1% and the detection accuracy is increased by 3% compared to the baseline model. We test it on the challenging BDD100K dataset and the SODA10M dataset, and the results show that DF-YOLO has excellent generalization ability.