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

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 c...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 6; pp. 1443 - 1453
Main Authors: Yu, Anjun, Gao, Yixiang, Xiong, Yonghua, Liu, Wei, She, Jinhua
Format: Journal Article
Language:English
Published: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Subjects:
Complexity
Electronic devices
Embedded systems
Inference
Maintenance management
Object recognition
Real time
Road maintenance
Roads & highways
Service life
ISSN:1343-0130, 1883-8014
Online Access:Get full text
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Summary: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.
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p1443