Multiple distresses detection for Asphalt Pavement using improved you Only Look Once Algorithm based on convolutional neural network

• Published in: The international journal of pavement engineering Vol. 25; no. 1
• Main Authors: Dan, Han-Cheng, Yan, Peng, Tan, Jiawei, Zhou, Yinchao, Lu, Bingjie
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2024; Taylor & Francis LLC
• Subjects: Algorithms; Artificial neural networks; Asphalt pavements; Clustering; CSPNext; Data augmentation; Datasets; Highway maintenance; Image processing; Image processing systems; Image quality; Machine learning; Object recognition; Pavement distress detection; image recognition; secondary labelling; YOLOv7
• ISSN: 1029-8436; 1477-268X
• DOI: 10.1080/10298436.2024.2308169

Leveraging the YOLOv7 object detection framework, this study introduces YOLOv7-CSP, a refined algorithm tailored for identifying asphalt pavement distress with enhanced precision. Utilizing advanced image processing for dataset preprocessing, including data augmentation and denoising, YOLOv7-CSP integrates the CSPNeXt structure and CA attention mechanism for improved detection accuracy and efficiency. The algorithm optimizes anchor box selection through Kmeans clustering and employs a secondary labeling method to enhance learning efficiency and dataset quality. Comparative tests reveal YOLOv7-CSP's superior performance, with significant improvements in mAP, F1 score, GFLOPS, and FPS metrics, demonstrating its effectiveness in detecting various pavement distresses. This innovative approach marks a significant advancement in automatic pavement distress recognition, offering a robust solution for highway maintenance decision-making.