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Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7
Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement de...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459580/ https://www.ncbi.nlm.nih.gov/pubmed/37631649 http://dx.doi.org/10.3390/s23167112 |
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author | Huang, Peile Wang, Shenghuai Chen, Jianyu Li, Weijie Peng, Xing |
author_facet | Huang, Peile Wang, Shenghuai Chen, Jianyu Li, Weijie Peng, Xing |
author_sort | Huang, Peile |
collection | PubMed |
description | Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement defect detection model based on an improved YOLOv7 architecture. The model introduces four key enhancements: first, the incorporation of the SPPCSPC_Group grouped space pyramid pooling module to reduce the parameter load and computational complexity; second, the utilization of the K-means clustering algorithm for generating anchors, accelerating model convergence; third, the integration of the Ghost Conv module, enhancing feature extraction while minimizing the parameters and calculations; fourth, introduction of the CBAM convolution module to enrich the semantic information in the last layer of the backbone network. The experimental results demonstrate that the improved model achieved an average accuracy of 91%, and the accuracy in detecting broken plates and repaired models increased by 9% and 8%, respectively, compared to the original model. Moreover, the improved model exhibited reductions of 14.4% and 29.3% in the calculations and parameters, respectively, and a 29.1% decrease in the model size, resulting in an impressive 80 FPS (frames per second). The enhanced YOLOv7 successfully balances parameter reduction and computation while maintaining high accuracy, making it a more suitable choice for pavement defect detection compared with other algorithms. |
format | Online Article Text |
id | pubmed-10459580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104595802023-08-27 Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 Huang, Peile Wang, Shenghuai Chen, Jianyu Li, Weijie Peng, Xing Sensors (Basel) Article Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement defect detection model based on an improved YOLOv7 architecture. The model introduces four key enhancements: first, the incorporation of the SPPCSPC_Group grouped space pyramid pooling module to reduce the parameter load and computational complexity; second, the utilization of the K-means clustering algorithm for generating anchors, accelerating model convergence; third, the integration of the Ghost Conv module, enhancing feature extraction while minimizing the parameters and calculations; fourth, introduction of the CBAM convolution module to enrich the semantic information in the last layer of the backbone network. The experimental results demonstrate that the improved model achieved an average accuracy of 91%, and the accuracy in detecting broken plates and repaired models increased by 9% and 8%, respectively, compared to the original model. Moreover, the improved model exhibited reductions of 14.4% and 29.3% in the calculations and parameters, respectively, and a 29.1% decrease in the model size, resulting in an impressive 80 FPS (frames per second). The enhanced YOLOv7 successfully balances parameter reduction and computation while maintaining high accuracy, making it a more suitable choice for pavement defect detection compared with other algorithms. MDPI 2023-08-11 /pmc/articles/PMC10459580/ /pubmed/37631649 http://dx.doi.org/10.3390/s23167112 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Peile Wang, Shenghuai Chen, Jianyu Li, Weijie Peng, Xing Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title | Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title_full | Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title_fullStr | Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title_full_unstemmed | Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title_short | Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7 |
title_sort | lightweight model for pavement defect detection based on improved yolov7 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459580/ https://www.ncbi.nlm.nih.gov/pubmed/37631649 http://dx.doi.org/10.3390/s23167112 |
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