{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:20:10Z","timestamp":1770351610807,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Defense Science and Technology Outstanding Youth Foundation","award":["2022-JCJQ-ZQ-002"],"award-info":[{"award-number":["2022-JCJQ-ZQ-002"]}]},{"name":"National Defense Science and Technology Outstanding Youth Foundation","award":["2023AFB446"],"award-info":[{"award-number":["2023AFB446"]}]},{"name":"National Defense Science and Technology Outstanding Youth Foundation","award":["614221722050603"],"award-info":[{"award-number":["614221722050603"]}]},{"name":"National Defense Science and Technology Outstanding Youth Foundation","award":["202250E060"],"award-info":[{"award-number":["202250E060"]}]},{"name":"Hubei Province Natural Science Foundation","award":["2022-JCJQ-ZQ-002"],"award-info":[{"award-number":["2022-JCJQ-ZQ-002"]}]},{"name":"Hubei Province Natural Science Foundation","award":["2023AFB446"],"award-info":[{"award-number":["2023AFB446"]}]},{"name":"Hubei Province Natural Science Foundation","award":["614221722050603"],"award-info":[{"award-number":["614221722050603"]}]},{"name":"Hubei Province Natural Science Foundation","award":["202250E060"],"award-info":[{"award-number":["202250E060"]}]},{"name":"National Key Laboratory of Science and Technology","award":["2022-JCJQ-ZQ-002"],"award-info":[{"award-number":["2022-JCJQ-ZQ-002"]}]},{"name":"National Key Laboratory of Science and Technology","award":["2023AFB446"],"award-info":[{"award-number":["2023AFB446"]}]},{"name":"National Key Laboratory of Science and Technology","award":["614221722050603"],"award-info":[{"award-number":["614221722050603"]}]},{"name":"National Key Laboratory of Science and Technology","award":["202250E060"],"award-info":[{"award-number":["202250E060"]}]},{"name":"Projects Foundation of University","award":["2022-JCJQ-ZQ-002"],"award-info":[{"award-number":["2022-JCJQ-ZQ-002"]}]},{"name":"Projects Foundation of University","award":["2023AFB446"],"award-info":[{"award-number":["2023AFB446"]}]},{"name":"Projects Foundation of University","award":["614221722050603"],"award-info":[{"award-number":["614221722050603"]}]},{"name":"Projects Foundation of University","award":["202250E060"],"award-info":[{"award-number":["202250E060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities. To handle the issue of complex environments, the backbone employs GhostHGNetv2 to capture more accurate high-level and low-level feature representations, facilitating better distinction between background and targets. In addition, this network significantly reduces both network parameter size and computational complexity. To address the issue of varying target scales, the first layer of the feature fusion module introduces Multi-Scale Convolutional Attention (MSCA) to capture multi-scale contextual information and guide the feature fusion process. A new lightweight detection head, Shared Convolutional Detection Head (SCDH), is designed to enable the model to achieve higher accuracy while being lighter. To evaluate the performance of this algorithm, a dataset for object detection in this scenario was constructed. The experiment results indicate that compared to the original model, the parameter number of the improved model decreased by 37.8%, the GFLOPs decreased by 27.7%, and the average accuracy increased from 82.7% to 85.0%. This validates the accuracy and applicability of the proposed GMS-YOLO network.<\/jats:p>","DOI":"10.3390\/s24175789","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T11:23:19Z","timestamp":1725535399000},"page":"5789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["GMS-YOLO: An Algorithm for Multi-Scale Object Detection in Complex Environments in Confined Compartments"],"prefix":"10.3390","volume":"24","author":[{"given":"Qixiang","family":"Ding","sequence":"first","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Weichao","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-434X","authenticated-orcid":false,"given":"Chengcheng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Mingyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Changchong","family":"Sheng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Min","family":"He","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China"}]},{"given":"Nanliang","family":"Shan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ramezani, F., Parvez, S., Fix, J.P., Battaglin, A., Whyte, S., Borys, N.J., and Whitaker, B.M. (2023). Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-28664-3"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103514","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","volume":"126","author":"Zaidi","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_9","first-page":"1","article-title":"Mask YOLOv7-Based Drone Vision System for Automated Cattle Detection and Counting","volume":"2","author":"Bello","year":"2024","journal-title":"Artif. Intell. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108914","DOI":"10.1016\/j.compag.2024.108914","article-title":"A lightweight improved YOLOv5s model and its deployment for detecting pitaya fruits in daytime and nighttime light-supplement environments","volume":"220","author":"Li","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","first-page":"221","article-title":"Real-Time Human Detection and Counting System Using Deep Learning Computer Vision Techniques","volume":"1","author":"Mokayed","year":"2023","journal-title":"Artif. Intell. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"93215","DOI":"10.1109\/ACCESS.2023.3309693","article-title":"Insulator and Defect Detection Model Based on Improved YOLO-S","volume":"11","author":"Yi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"1","article-title":"Fault Detection Method of Glass Insulator Aerial Image Based on the Improved YOLOv5","volume":"72","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.1109\/JSEN.2021.3073422","article-title":"Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection","volume":"21","author":"Gao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_15","first-page":"1","article-title":"An Insulator Defect Detection Model in Aerial Images Based on Multiscale Feature Pyramid Network","volume":"71","author":"Hao","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_17","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15\u201320). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_19","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019). Learning Spatial Fusion for Single-Shot Object Detection. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cheng, G., Lang, C., Wu, M., Xie, X., Yao, X., and Han, J. (2021). Feature Enhancement Network for Object Detection in Optical Remote Sensing Images. J. Remote Sens., 2021.","DOI":"10.34133\/2021\/9805389"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, K., and Shen, H. (2022). Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14030579"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chang, J., Lu, Y., Xue, P., Xu, Y., and Wei, Z. (2021). ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN. arXiv.","DOI":"10.1007\/s10489-022-03508-1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, Y., Li, Q., and Yan, J. (2020). DMCP: Differentiable Markov Channel Pruning for Neural Networks. arXiv.","DOI":"10.1109\/CVPR42600.2020.00161"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., and Sun, J. (2017, January 22\u201329). Channel Pruning for Accelerating Very Deep Neural Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C. (2017, January 22\u201329). Learning Efficient Convolutional Networks through Network Slimming. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref_27","unstructured":"Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). GhostNet: More Features from Cheap Operations. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ren, F., Yao, M., Doma, B., Zhang, T., Wang, J., and Liu, S. (2023, January 24\u201326). Research on Algorithm for License Plate Detection in Complex Scenarios Based on Artificial Intelligence. Proceedings of the 2023 3rd International Conference on Communication Technology and Information Technology (ICCTIT), Xi\u2019an, China.","DOI":"10.1109\/ICCTIT60726.2023.10435878"},{"key":"ref_31","first-page":"683","article-title":"Semantic Segmentation by Using Down-Sampling and Subpixel Convolution: DSSC-UNet","volume":"75","author":"Kwon","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1007\/s00034-020-01630-4","article-title":"Image Super-Resolution Based on the Down-Sampling Iterative Module and Deep CNN","volume":"40","author":"Yang","year":"2021","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_33","first-page":"1140","article-title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention Mechanisms in Computer Vision: A Survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_36","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","article-title":"PCT: Point cloud transformer","volume":"7","author":"Guo","year":"2021","journal-title":"Comput. Vis. Media"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2017, January 21\u201326). SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters\u2014Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhang, L., Cheng, M.M., and Feng, J. (2020, January 13\u201319). Strip Pooling: Rethinking Spatial Pooling for Scene Parsing. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_45","first-page":"1922","article-title":"FCOS: A simple and strong anchor-free object detector","volume":"44","author":"Tian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","unstructured":"Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., Chen, K., Conv, C., and Concat, R. (2022). RTMDet: An Empirical Study of Designing Real-Time Object Detectors. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/TPWRD.2023.3328178","article-title":"MFI-YOLO: Multi-fault insulator detection based on an improved YOLOv8","volume":"39","author":"He","year":"2023","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, H., Jia, Z., and Li, Z. (2023). BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8. Sensors, 10.","DOI":"10.3390\/s23208361"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, H., Chen, J., Hu, J., and Zheng, E. (2023). Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion. Electronics, 12.","DOI":"10.3390\/electronics12153210"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lou, H., Guo, J., Chen, H., Liu, H., Gu, J., Bi, L., and Duan, X. (2023). CS-YOLO: A new detection algorithm for alien intrusion on highway. Sci. Rep., 13.","DOI":"10.21203\/rs.3.rs-2795266\/v1"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:49:24Z","timestamp":1760111364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,5]]},"references-count":50,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175789"],"URL":"https:\/\/doi.org\/10.3390\/s24175789","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,5]]}}}