Multiple distresses detection for asphalt pavement using improved You Only Look Once algorithm based on convolutional neural network

• Main Authors: Dan, Han-Cheng, Yan, Peng, Tan, Jiawei, Zhou, Yinchao, Lu, Bingjie
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Pavement distress detection; Image recognition
• Online Access: https://hdl.handle.net/10356/178281
• Institution: Nanyang Technological University

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.