Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm

Weiquan Lu; Jiarong Zhang; Jinli Ju; Ziyu Liu; Yuxiang Zhao; Rong Qin; Danni Lu; Xiaolu Zhou

doi:10.1117/12.3031016

19 July 2024 Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm

Weiquan Lu, Jiarong Zhang, Jinli Ju, Ziyu Liu, Yuxiang Zhao, Rong Qin, Danni Lu, Xiaolu Zhou

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810N (2024) https://doi.org/10.1117/12.3031016
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In the application of computer vision recognition, the recognition objects of some tissue-like structures present highly complex and variable characteristics. For this practical usage scenario, traditional classification algorithms usually cannot achieve the desired recognition effect. To achieve better recognition performance in this direction, this study integrates ASFF into the YOLOv5 model and introduces the SimAm attention mechanism module. The improved network structure is more lightweight, has fewer network parameters, and the attention mechanism is more effective. We trained the modified YOLO model on datasets, and experimental results show a significant increase in mAP value and a 20% increase in accuracy. This indicates that improvements to the model can significantly enhance performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weiquan Lu, Jiarong Zhang, Jinli Ju, Ziyu Liu, Yuxiang Zhao, Rong Qin, Danni Lu, and Xiaolu Zhou "Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810N (19 July 2024); https://doi.org/10.1117/12.3031016

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