{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:39:17Z","timestamp":1771029557502,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61461053"],"award-info":[{"award-number":["61461053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this task, but most of them are either highly accurate and slow in detection, or the accuracy is low and the detection speed is high. The accuracy and speed have achieved good results, but the generalization of the model to other datasets is poor. Given this, this paper takes YOLOv5s as a benchmark model and proposes an optimization model to solve the problem of road defect detection. First, we significantly reduce the parameters of the model by pruning the model and removing unimportant modules, propose an improved Spatial Pyramid Pooling-Fast (SPPF) module to improve the feature signature fusion ability, and finally add an attention module to focus on the key information. The activation function, sampling method, and other strategies were also replaced in this study. The test results on the Global Road Damage Detection Challenge (GRDDC) dataset show that the FPS of our proposed model is not only faster than the baseline model but also improves the MAP by 2.08%, and the size of this model is also reduced by 6.07 M.<\/jats:p>","DOI":"10.3390\/e25091280","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T09:14:41Z","timestamp":1693473281000},"page":"1280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["HE-YOLOv5s: Efficient Road Defect Detection Network"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4908-7277","authenticated-orcid":false,"given":"Yonghao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information, Yunnan University, Kunming 650500, China"}]},{"given":"Minglei","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Information, Yunnan University, Kunming 650500, China"},{"name":"Yunnan Province Highway Networking Charge Management Co., Kunming 650000, China"}]},{"given":"Guangen","family":"Ding","sequence":"additional","affiliation":[{"name":"Yunnan Province Highway Networking Charge Management Co., Kunming 650000, China"}]},{"given":"Hongwei","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Information, Yunnan University, Kunming 650500, China"}]},{"given":"Peng","family":"Hu","sequence":"additional","affiliation":[{"name":"Research and Development Department, Youbei Technology Co., Kunming 650000, China"}]},{"given":"Hongzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Anti-Jamming, University of Electronic Science and Technology, Chengdu 610000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/15389581003754593","article-title":"Road safety research in China: Review and appraisal","volume":"11","author":"Wang","year":"2010","journal-title":"Traffic Inj. Prev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4708","DOI":"10.1016\/j.trpro.2017.05.484","article-title":"Road traffic accidents in India: Issues and challenges","volume":"25","author":"Singh","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_3","first-page":"141","article-title":"Cost of crashes related to road conditions, United States, 2006","volume":"Volume 53","author":"Zaloshnja","year":"2009","journal-title":"Annals of Advances in Automotive Medicine\/Annual Scientific Conference"},{"key":"ref_4","first-page":"323","article-title":"Road traffic accidents: Study of risk factors","volume":"14","author":"Khan","year":"2007","journal-title":"Prof. Med. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14531","DOI":"10.1109\/ACCESS.2020.2966881","article-title":"Review of pavement defect detection methods","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Guo, X., Hou, F., and Wu, J. (2022). Review of intelligent road defects detection technology. Sustainability, 14.","DOI":"10.3390\/su14106306"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104190","DOI":"10.1016\/j.autcon.2022.104190","article-title":"Machine learning techniques for pavement condition evaluation","volume":"136","author":"Sholevar","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bello-Salau, H., Aibinu, A.M., Onwuka, E.N., Dukiya, J.J., and Onumanyi, A.J. (October, January 29). Image processing techniques for automated road defect detection: A survey. Proceedings of the 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria.","DOI":"10.1109\/ICECCO.2014.6997556"},{"key":"ref_9","unstructured":"Chatterjee, S., Saeedfar, P., Tofangchi, S., and Kolbe, L.M. (2018, January 23\u201328). Intelligent Road Maintenance: A Machine Learning Approach for surface Defect Detection. Proceedings of the ECIS 2018, Portsmouth, UK."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1109\/TITS.2018.2856928","article-title":"Automatic pavement crack detection by multi-scale image fusion","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24452","DOI":"10.1109\/ACCESS.2018.2829347","article-title":"Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods","volume":"6","author":"Ai","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U., and Gross, H.M. (2017, January 14\u201319). How to get pavement distress detection ready for deep learning? A systematic approach. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966101"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.conbuildmat.2017.09.110","article-title":"Deep convolutional neural networks with transfer learning for computer vision based data driven pavement distress detection","volume":"157","author":"Gopalakrishnan","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114892","DOI":"10.1109\/ACCESS.2020.3003638","article-title":"Automated pavement crack segmentation using u-net-based convolutional neural network","volume":"8","author":"Lau","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1111\/mice.12622","article-title":"Automated pavement crack detection and segmentation based on two-step convolutional neural network","volume":"35","author":"Liu","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1177\/03611981211004974","article-title":"Deep convolutional neural networks for pavement crack detection using an inexpensive global shutter RGB-D sensor and ARM-based single-board computer","volume":"2675","author":"Asadi","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"ref_17","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_19","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_20","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_21","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/hash\/14bfa6bb14875e45bba028a21ed38046-Abstract.html."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 19\u201325). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_23","unstructured":"Jiang, Z., Zhao, L., Li, S., and Jia, Y. (2020). Real-time object detection method based on improved YOLOv4-tiny. arXiv."},{"key":"ref_24","first-page":"1","article-title":"YOLOv4-5D: An effective and efficient object detector for autonomous driving","volume":"70","author":"Cai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 20\u201325). TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Nashville, TN, USA.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_26","unstructured":"Ge, Z., Liu, S., Wang, F., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107622","DOI":"10.1016\/j.patcog.2020.107622","article-title":"Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation","volume":"110","author":"Lian","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_34","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, Springer International Publishing."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_36","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019). Learning spatial fusion for single-shot object detection. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8326","DOI":"10.1109\/TIP.2020.3013162","article-title":"Matnet: Motion-attentive transition network for zero-shot video object segmentation","volume":"29","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, L., and Li, Y. (2021, January 4\u20136). Micro-YOLO: Exploring Efficient Methods to Compress CNN based Object Detection Model. Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), Online.","DOI":"10.5220\/0010234401510158"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1007\/s11119-020-09754-y","article-title":"Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model","volume":"22","author":"Fu","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhong, Y., and Li, X. (2019, January 27\u201328). SlimYOLOv3: Narrower, faster and better for real-time UA V applications. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00011"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, X., and Zhang, T. (2022). Lite-yolov5: A lightweight deep learning detector for on-board ship detection in large-scene sentinel-1 sar images. Remote Sens., 14.","DOI":"10.3390\/rs14041018"},{"key":"ref_42","unstructured":"Ma, N., Zhang, X., and Sun, J. (2020). Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI 16, Springer International Publishing."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 20\u201325). Bottleneck transformers for visual recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Voita, E., Talbot, D., Moiseev, F., Sennrich, R., and Titov, I. (2019). Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. arXiv.","DOI":"10.18653\/v1\/P19-1580"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/TIP.2021.3132834","article-title":"Group-Wise Learning for Weakly Supervised Semantic Segmentation","volume":"31","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_48","unstructured":"Jaderberg, M., Simonyan, K., and Zisserman, A. (2015). Spatial transformer networks. Adv. Neural Inf. Process. Syst., 28, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/hash\/33ceb07bf4eeb3da587e268d663aba1a-Abstract.html."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_50","unstructured":"Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-based attention module. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, Q.L., and Yang, Y.B. (2021, January 6\u201311). Sa-net: Shuffle attention for deep convolutional neural networks. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_52","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arya, D., Maeda, H., Ghosh, S.K., Toshniwal, D., Omata, H., Kashiyama, T., and Sekimoto, Y. (2020, January 10\u201313). Global Road Damage Detection: State-of-the-art Solutions. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9377790"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1280\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:43:45Z","timestamp":1760129025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["e25091280"],"URL":"https:\/\/doi.org\/10.3390\/e25091280","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,31]]}}}