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Pavement Mixed Disease Algorithm Based on Improved YOLOv9-c
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Ying ZHANG1, 2, Ji-xu WANG1, Ying-kang CAO1, Gang LI1, You-liang FANG2, *
Science Technology and Engineering | 2025, 25(18) : 7793 - 7802
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Science Technology and Engineering | 2025, 25(18): 7793-7802
• Papers·Traffics and Transportations •
Pavement Mixed Disease Algorithm Based on Improved YOLOv9-c
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Ying ZHANG1, 2, Ji-xu WANG1, Ying-kang CAO1, Gang LI1, You-liang FANG2, *
Affiliations
  • 1 Department of Civil Engineering and Architecture, Hebei University, Baoding 071002, China
  • 2 Engineering Research Center of Zero-Carbon Energy Buildings and Measurement Techniques, Ministry of Education, Hebei University, Baoding 071002, China
Published: 2025-06-28 doi: 10.12404/j.issn.1671-1815.2407398
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Aiming at the problems of poor real-time detection, low accuracy, and false detection and omission of pavement disease detection including hole and crack, an improved algorithm based on YOLOv9 was proposed to resolve the problem. Firstly, AKConv (alterable kernel convolution) was introduced into the backbone network to replace the convolution module in RepNCSPELAN4, which improves the feature extraction ability of the network for different diseases and effectively solve the problem that road disease is difficult to distinguish from background environment features. Secondly, selective image attention mechanism (SimAM) and DySample sampling modules were introduced to focus on the key information in the detection head, and the capability to extract information features was enhanced more efficiently. Finally, the inner-IOU function was used to optimize the weight parameters of the model to improve the learning ability of mixed samples. The experimental comparison between YOLOv9-c and our model showed that the accuracy, recall rate and MAP of the improved model are increased by 40.17%, 15.99% and 20.95% respectively. The performance has been significantly improved, and the detection effect is more accurately and efficiently, and the accuracy and generalization ability of pavement disease detection algorithm are improved.

YOLOv9-c  /  pavement mixed disease  /  attention mechanism  /  feature extraction  /  loss function
Ying ZHANG, Ji-xu WANG, Ying-kang CAO, Gang LI, You-liang FANG. Pavement Mixed Disease Algorithm Based on Improved YOLOv9-c[J]. Science Technology and Engineering, 2025 , 25 (18) : 7793 -7802 . DOI: 10.12404/j.issn.1671-1815.2407398
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Year 2025 volume 25 Issue 18
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doi: 10.12404/j.issn.1671-1815.2407398
  • Receive Date：2024-10-08
  • Online Date：2025-12-17
  • Published：2025-06-28
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  • Received：2024-10-08
  • Revised：2025-04-03
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Affiliations
    1 Department of Civil Engineering and Architecture, Hebei University, Baoding 071002, China
    2 Engineering Research Center of Zero-Carbon Energy Buildings and Measurement Techniques, Ministry of Education, Hebei University, Baoding 071002, China
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