{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:46:52Z","timestamp":1771458412765,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51675167"],"award-info":[{"award-number":["51675167"]}]},{"name":"National Natural Science Foundation of China","award":["2021BAA056"],"award-info":[{"award-number":["2021BAA056"]}]},{"name":"National Natural Science Foundation of China","award":["2020CFB755"],"award-info":[{"award-number":["2020CFB755"]}]},{"name":"National Natural Science Foundation of China","award":["T2020018"],"award-info":[{"award-number":["T2020018"]}]},{"name":"National Natural Science Foundation of China","award":["Q20191801"],"award-info":[{"award-number":["Q20191801"]}]},{"name":"Key Research and Development Project of Hubei Province of China","award":["51675167"],"award-info":[{"award-number":["51675167"]}]},{"name":"Key Research and Development Project of Hubei Province of China","award":["2021BAA056"],"award-info":[{"award-number":["2021BAA056"]}]},{"name":"Key Research and Development Project of Hubei Province of China","award":["2020CFB755"],"award-info":[{"award-number":["2020CFB755"]}]},{"name":"Key Research and Development Project of Hubei Province of China","award":["T2020018"],"award-info":[{"award-number":["T2020018"]}]},{"name":"Key Research and Development Project of Hubei Province of China","award":["Q20191801"],"award-info":[{"award-number":["Q20191801"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["51675167"],"award-info":[{"award-number":["51675167"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["2021BAA056"],"award-info":[{"award-number":["2021BAA056"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["2020CFB755"],"award-info":[{"award-number":["2020CFB755"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["T2020018"],"award-info":[{"award-number":["T2020018"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["Q20191801"],"award-info":[{"award-number":["Q20191801"]}]},{"name":"Research Project of the Education Department of Hubei Province of China","award":["51675167"],"award-info":[{"award-number":["51675167"]}]},{"name":"Research Project of the Education Department of Hubei Province of China","award":["2021BAA056"],"award-info":[{"award-number":["2021BAA056"]}]},{"name":"Research Project of the Education Department of Hubei Province of China","award":["2020CFB755"],"award-info":[{"award-number":["2020CFB755"]}]},{"name":"Research Project of the Education Department of Hubei Province of China","award":["T2020018"],"award-info":[{"award-number":["T2020018"]}]},{"name":"Research Project of the Education Department of Hubei Province of China","award":["Q20191801"],"award-info":[{"award-number":["Q20191801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23167112","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T12:10:23Z","timestamp":1691755823000},"page":"7112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7"],"prefix":"10.3390","volume":"23","author":[{"given":"Peile","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghuai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","first-page":"346","article-title":"The Ministry of Transport issued the \u201cStatistical Bulletin on the Development of the Transportation Industry in 2021\u201d","volume":"43","author":"Yin","year":"2022","journal-title":"Shuidao Port"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106688","DOI":"10.1016\/j.jobe.2023.106688","article-title":"Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm","volume":"73","author":"Ye","year":"2023","journal-title":"J. Build. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, H., Zhang, Y., Zhang, D., Ouyang, H., and Chen, X. (2022). A Multiscale Lightweight and Efficient Model Based on YOLOv7: Applied to Citrus Orchard. Plants, 11.","DOI":"10.3390\/plants11233260"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xu, S., Song, W., He, Q., and Wei, Q. (2021). Lightweight underwater object detection based on yolo v4 and multi-scale attentional feature fusion. Remote Sens., 13.","DOI":"10.3390\/rs13224706"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., and Liu, X. (2023). An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians. Sensors, 23.","DOI":"10.3390\/s23135912"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"950","DOI":"10.23919\/JSEE.2020.000063","article-title":"Lira-YOLO: A lightweight model for ship detection in radar images","volume":"31","author":"Zhou","year":"2020","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Du, F.J., and Jiao, S.J. (2022). Improvement of lightweight convolutional neural network model based on YOLO algorithm and its research in pavement defect detection. Sensors, 22.","DOI":"10.3390\/s22093537"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jiang, J., Fu, X., Qin, R., Wang, X., and Ma, Z. (2021). High-speed lightweight ship detection algorithm based on YOLO-v4 for three-channels RGB SAR image. Remote Sens., 13.","DOI":"10.3390\/rs13101909"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13634-022-00931-x","article-title":"YOLO-LRDD: A lightweight method for road damage detection based on improved YOLOv5s","volume":"2022","author":"Wan","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/ACCESS.2019.2961959","article-title":"Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments","volume":"8","author":"Fang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1049\/cvi2.12072","article-title":"TRC-YOLO: A real-time detection method for lightweight targets based on mobile devices","volume":"16","author":"Wang","year":"2022","journal-title":"IET Comput. Vis."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xia, Y., Nguyen, M., and Yan, W.Q. (2022, January 24\u201325). A Real-Time Kiwifruit Detection Based on Improved YOLOv7. Proceedings of the Image and Vision Computing: 37th International Conference, IVCNZ 2022, Auckland, New Zealand. Revised Selected Papers.","DOI":"10.1007\/978-3-031-25825-1_4"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"166","DOI":"10.54254\/2755-2721\/22\/20231212","article-title":"Improved YOLOv7-tiny object detection lightweight model","volume":"59","author":"Liu","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Duan, B., and Ma, M. (2023). Research on Mask Detection Based on Improved YOLOv5 Algorithm. Comput. Eng. Appl., 1\u201311.","DOI":"10.54254\/2755-2721\/6\/20230304"},{"key":"ref_16","first-page":"187","article-title":"High-precision Garbage Detection Algorithm of Lightweight YOLOv5n","volume":"59","author":"Tu","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22166","DOI":"10.1109\/TITS.2022.3161960","article-title":"Automatic Detection and Counting System for Pavement Cracks Based on PCGAN and YOLO-MF","volume":"23","author":"Ma","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kaya, \u00d6., \u00c7odur, M.Y., and Mustafaraj, E. (2023). Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7. Buildings, 13.","DOI":"10.3390\/buildings13041070"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.engstruct.2022.115406","article-title":"Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model","volume":"277","author":"Que","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_20","first-page":"215","article-title":"FS-YOLOv5: Lightweight Infrared Object Detection Method","volume":"59","author":"Huang","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_21","first-page":"135","article-title":"Lightweight pineapple heart detection algorithm based on improved YOLOv4","volume":"39","author":"Zhang","year":"2023","journal-title":"J. Agric. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, C., Ye, M., Zhang, J., and Ma, Y. (2023). YOLO-LWNet: A lightweight road damage object detection network for mobile terminal devices. Sensors, 23.","DOI":"10.3390\/s23063268"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133936","DOI":"10.1109\/ACCESS.2022.3230894","article-title":"Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:33Z","timestamp":1760128293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":23,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167112"],"URL":"https:\/\/doi.org\/10.3390\/s23167112","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}