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

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

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 int...

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Bibliographic Details
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
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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