Mobile-YOLO: A Lightweight YOLO for Road Crack Detection on Mobile Devices

抄録

Road crack detection is critical for ensuring road traffic safety, extending the service life of roads, and improving the efficiency of road maintenance management. However, the traditional YOLOv8 model, when applied on mobile devices, faces challenges, such as high network complexity, significant computational resource demands, and slow inference speeds owing to limited computational resources. To address these issues, this paper proposes a model tailored for mobile terminals—Mobile-YOLO. By incorporating the universal inverted bottleneck module and the multi-query attention mechanism, the model significantly reduces network complexity while enhancing computational efficiency for mobile deployment, making it well suited for real-time detection requirements in embedded systems and vehicle-mounted patrol devices. Experimental results showed that Mobile-YOLO improves detection accuracy by 4.1%, mAP50 by 2.76%, and mAP50-95 by 2.56% compared with the baseline YOLOv8, achieving an inference speed of 113 fps, outperforming other lightweight models. Experiments on the NVIDIA Jetson Nano platform further validated its excellent inference performance and low false positive rate, providing an efficient solution for real-world road crack detection in resource-constrained environments.