{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:01:57Z","timestamp":1789642917054,"version":"4.0.0"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Provincial Science Foundation of Sichuan","doi-asserted-by":"publisher","award":["2025ZNSFSC1445"],"award-info":[{"award-number":["2025ZNSFSC1445"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Personal protective equipment (PPE) detection in industrial scenes is a joint classification-and-localization problem complicated by partial occlusion, scale variation, and background clutter. PPE categories also follow structured physical relations; for example, helmets are normally associated with heads and gloves with hands. Existing attention modules mainly reweight appearance features and seldom encode these directional inter-class dependencies. We therefore propose IRA-YOLO, a YOLO-based object detector equipped with an Inter-Class Relation Attention Module (IRAM). The IRAM projects each multi-scale feature map into class-specific subspaces, learns asymmetric pairwise relations among category attention maps, and returns the relational prior to the detection feature through residual fusion. On SH17, IRA-YOLO improves mAP@50:95 from 0.396 to 0.411 over YOLOv11s while adding 0.42 M parameters and 0.99 GFLOPs, and it maintains 139.83 FPS in the single-image inference test. Cross-dataset evaluation on CHVG further supports the transferability of the proposed feature refinement. These results indicate that explicit inter-class relation modeling provides complementary semantic context for multi-class PPE detection.<\/jats:p>","DOI":"10.3390\/s26185882","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T09:59:03Z","timestamp":1789639143000},"page":"5882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IRA-YOLO: Inter-Class Relation Attention for PPE Detection in Industrial Scenes"],"prefix":"10.3390","volume":"26","author":[{"given":"Ran","family":"Zhao","sequence":"first","affiliation":[{"name":"Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250098, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Haoran","family":"Duan","sequence":"additional","affiliation":[{"name":"Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250098, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250098, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Nan","family":"Xu","sequence":"additional","affiliation":[{"name":"Anshi Technology Co., Ltd., Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Junlin","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1631-3637","authenticated-orcid":false,"given":"Pei","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100980","DOI":"10.1016\/j.aei.2019.100980","article-title":"Computer Vision for Behaviour-Based Safety in Construction: A Review and Future Directions","volume":"43","author":"Fang","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9703560","DOI":"10.1155\/2020\/9703560","article-title":"Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks","volume":"2020","author":"Li","year":"2020","journal-title":"Adv. Civ. Eng."},{"key":"ref_3","unstructured":"Jocher, G. (2026, July 15). YOLOv5 by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_4","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, July 15). YOLOv8 by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv.","DOI":"10.52202\/079017-3429"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","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_9","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_10","unstructured":"Santoro, A., Raposo, D., Barrett, D.G.T., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T. (2017, January 4\u20139). A Simple Neural Network Module for Relational Reasoning. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_11","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_12","first-page":"175","article-title":"SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry","volume":"6","author":"Ahmad","year":"2024","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_13","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Montr\u00e9al, QC, Canada."},{"key":"ref_14","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_16","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 (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_17","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_18","unstructured":"Wang, Z., Zheng, C., Zhang, Y., Cui, Z., and Li, H. (2024). Mamba-YOLO: SSMs-Based YOLO for Object Detection. arXiv."},{"key":"ref_19","unstructured":"Lv, W., Xu, S., Zhao, Y., Wang, G., Wei, J., Cui, C., Du, Y., Dang, Q., and Liu, Y. (2024, January 20\u201324). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WD, USA."},{"key":"ref_20","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 (CVPR), Seattle, WD, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_21","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), Virtual.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_22","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018, January 3\u20136). BAM: Bottleneck Attention Module. Proceedings of the British Machine Vision Conference (BMVC), Newcastle upon Tyne, UK."},{"key":"ref_23","unstructured":"Yang, L., Zhang, R.Y., Li, L., and Xie, X. (2021). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning (ICML), PMLR."},{"key":"ref_24","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 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Johnson, J., Gupta, A., and Fei-Fei, L. (2018, January 18\u201322). Image Generation from Scene Graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00133"},{"key":"ref_26","unstructured":"Teney, D., Anderson, P., He, X., and Hengel, A.v.d. (2020, January 14\u201319). Graph Neural Networks for Visual Question Answering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WD, USA."},{"key":"ref_27","unstructured":"Chen, Y., Wang, J., Peng, J., and Sun, J. (February, January 27). Graph-Based Reasoning for Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA."},{"key":"ref_29","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the Conference on Language Modeling (COLM), Philadelphia, PA, USA. 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