YOLOv8n-FAWL: Object Detection for Autonomous Driving Using YOLOv8 Network on Edge Devices

In the field of autonomous driving, common challenges include difficulties in detecting small vehicles and pedestrians on the road, high computational demands of algorithms, and low accuracy of detection algorithms. This paper proposes a YOLOv8n-FAWL object detection algorithm tailored for edge computing, incorporating the following three improvements: (1) The Faster-C2f-EMA module is created, designed through the synergy of the FasterNet architecture and the concept of EMA modules, effectively addressing the challenge of suboptimal feature extraction for small objects. (2) The WIOU loss function is adopted to resolve the issue of imbalanced training samples. (3) The LAMP pruning technique is applied to reduce the model parameters and complexity, thereby enhancing the overall model accuracy. The experimental results show that compared to the baseline model, the proposed algorithm achieves improvements of 6.2% and 4.5% in the mAP@0.5, and 3.8% and 2.7% in the mAP@0.5:0.95, on the Udacity and BDD100K-tiny datasets,respectively. In addition, the model parameters we’re reduced by 49.2% and 46%. The model achieved real-time performance at 54 FPS, thereby advancing the development of autonomous driving technology.