{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:05:15Z","timestamp":1791461115454,"version":"4.3.4"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Government of the Republic of Korea","award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}]},{"award":["IITP-2025-RS-2020-II201821"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201821"]}],"id":[{"id":"https:\/\/ror.org\/019xm3p48","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Industrial sites pose numerous hazards where unexpected accidents can occur at any time, and personal protective equipment (PPE) is a primary safeguard for worker safety. In this study, PPE specifically refers to safety helmets, safety shoes, and safety gloves. Manual verification of PPE usage is infeasible in environments with many workers, motivating automated detection. This study proposes a method that integrates the Convolutional Block Attention Module (CBAM) exclusively into the training-only auxiliary reversible branch of YOLOv9\u2019s Programmable Gradient Information (PGI) architecture. The proposed CBAMLinear module enhances gradient information during training while introducing zero additional computational overhead at inference, as the entire auxiliary branch is removed. The proposed YOLOv9 with CBAMLinear achieved consistent mAP@0.5:0.95 gains of 0.005\u20130.007 over the baseline for the three larger model variants, while maintaining identical inference-time parameters and FLOPs. In industrial safety, even modest performance gains can directly contribute to accident prevention by reducing false positives and false negatives, making this approach well suited for real-time safety management systems in industrial settings.<\/jats:p>","DOI":"10.3390\/s26103058","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"3058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Improved YOLOv9 Based Object Detection with Attention Mechanism for Personal Protective Equipment"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8319-4321","authenticated-orcid":false,"given":"Geunho","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3443-2074","authenticated-orcid":false,"given":"Jieun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8858-9046","authenticated-orcid":false,"given":"Tae-yong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4061-9532","authenticated-orcid":false,"given":"Jongpil","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1002\/ajim.23448","article-title":"Epidemiology of Occupational Injuries in Construction Workers between 2009 and 2018 in South Korea","volume":"66","author":"Jung","year":"2023","journal-title":"Am. J. Ind. Med."},{"key":"ref_2","first-page":"905","article-title":"Deep Learning-Based Framework for Monitoring Wearing Personal Protective Equipment on Construction Sites","volume":"10","author":"Lee","year":"2023","journal-title":"J. Comput. Des. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128102","DOI":"10.1016\/j.neucom.2024.128102","article-title":"Development and Challenges of Object Detection: A Survey","volume":"598","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, Z., Lei, X., and Kumar, M. (2025). Advancing Construction Safety: YOLOv8-CGS Helmet Detection Model. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0321713"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, H., Mu, C., Ma, X., Guo, X., and Hu, C. (2024). MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations. Appl. Sci., 14.","DOI":"10.3390\/app14114766"},{"key":"ref_8","first-page":"1","article-title":"YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information","volume":"Vol. 15089","author":"Leonardis","year":"2025","journal-title":"Computer Vision\u2014ECCV 2024"},{"key":"ref_9","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024, January 10\u201315). YOLOv10: Real-Time End-to-End Object Detection. Proceedings of the Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, BC, Canada."},{"key":"ref_10","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Eclarinal, J.C., and Alampay, R.B. (2023). YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images. Proceedings of the 2023 5th World Symposium on Software Engineering (WSSE \u201923), Tokyo, Japan, 22\u201324 September 2023, ACM.","DOI":"10.1145\/3631991.3632043"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8768","DOI":"10.1038\/s41598-024-58800-6","article-title":"Helmet Wearing Detection Algorithm Based on Improved YOLOv5","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pandey, A., Puri, M., and Varde, A. (2018, January 5\u20137). Object detection with neural models, deep learning and common sense to aid smart mobility. Proceedings of the 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece.","DOI":"10.1109\/ICTAI.2018.00134"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3991\/ijoe.v18i01.27577","article-title":"Computer Vision: A Review of Detecting Objects in Videos\u2013Challenges and Techniques","volume":"18","author":"Hammoudeh","year":"2022","journal-title":"Int. J. Online Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"170461","DOI":"10.1109\/ACCESS.2020.3021508","article-title":"Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review","volume":"8","author":"Aziz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_20","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 (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"Vol. 9905","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1038\/s41598-026-35148-7","article-title":"Domain-adaptive faster R-CNN for non-PPE identification on construction sites from body-worn and general images","volume":"16","author":"Wang","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120096","DOI":"10.1016\/j.eswa.2023.120096","article-title":"Verification and performance comparison of CNN-based algorithms for two-step helmet-wearing detection","volume":"225","author":"Lee","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107458","DOI":"10.1016\/j.compeleceng.2021.107458","article-title":"Method based on the cross-layer attention mechanism and multiscale perception for safety helmet-wearing detection","volume":"95","author":"Han","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_25","first-page":"5013","article-title":"RBFPDet: An anchor-free helmet wearing detection method","volume":"53","author":"Song","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24236","DOI":"10.1038\/s41598-025-08828-z","article-title":"A YOLOv8 algorithm for safety helmet wearing detection in complex environment","volume":"15","author":"Song","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","first-page":"213","article-title":"End-to-End Object Detection with Transformers","volume":"Vol. 12346","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201321). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"83535","DOI":"10.1007\/s11042-024-18872-y","article-title":"YOLO-based Object Detection Models: A Review and Its Applications","volume":"83","author":"Vijayakumar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","unstructured":"Khanam, R., and Hussain, M. (2024). What Is YOLOv5: A Deep Look into the Internal Features of the Popular Object Detector. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ling, H., Zhao, T., Zhang, Y., and Lei, M. (2024). Engineering Vehicle Detection Based on Improved YOLOv6. Appl. Sci., 14.","DOI":"10.3390\/app14178054"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1007\/978-3-030-58580-8_40","article-title":"Rethinking Bottleneck Structure for Efficient Mobile Network Design","volume":"Vol. 12348","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_35","unstructured":"Gomez, A.N., Ren, M., Urtasun, R., and Grosse, R.B. (2017, January 4\u20139). The Reversible Residual Network: Backpropagation Without Storing Activations. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A New Backbone That Can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, G., Ge, W., and Yu, Y. (2021, January 10\u201317). GraphFPN: Graph Feature Pyramid Network for Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00276"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5427","DOI":"10.1007\/s40747-024-01445-9","article-title":"An Attention Mechanism Module with Spatial Perception and Channel Information Interaction","volume":"10","author":"Wang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1109\/TII.2023.3347747","article-title":"PMSA-DyTr: Prior-Modulated and Semantic-Aligned Dynamic Transformer for Strip Steel Defect Detection","volume":"20","author":"Su","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Luong, T., Pham, H., and Manning, C.D. (2015, January 17\u201321). Effective Approaches to Attention-based Neural Machine Translation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5459","DOI":"10.1007\/s40747-024-01448-6","article-title":"TA-YOLO: A Lightweight Small Object Detection Model Based on Multi-Dimensional Trans-Attention Module for Remote Sensing Images","volume":"10","author":"Li","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11307","DOI":"10.1038\/s41598-020-67529-x","article-title":"Object Detection Based on an Adaptive Attention Mechanism","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gong, S., and Zheng, X. (2023, January 12\u201314). Enhancing Feature Extraction in DBNet: A Novel Approach with Squeeze-and-Excitation Block and CBAM Module. Proceedings of the 2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Dali, China.","DOI":"10.1109\/ICCASIT58768.2023.10351731"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"4851","DOI":"10.3390\/rs13234851","article-title":"ECAP-YOLO: Efficient Channel Attention Pyramid YOLO for Small Object Detection in Aerial Image","volume":"13","author":"Kim","year":"2021","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"Vol. 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, B., Sun, C.-F., Fang, S.-Q., Zhao, Y.-H., and Su, S. (2022). Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO. Sensors, 22.","DOI":"10.3390\/s22176702"},{"key":"ref_49","first-page":"530","article-title":"SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry","volume":"5","author":"Ahmad","year":"2024","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_50","unstructured":"Yu, F., Li, J., Wang, X., Li, D., Zhan, X., Wu, S., and Zhang, J. (2023). Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:42:13Z","timestamp":1779338533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,12]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26103058"],"URL":"https:\/\/doi.org\/10.3390\/s26103058","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,12]]}}}