{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:47:10Z","timestamp":1782406030933,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61762028"],"award-info":[{"award-number":["61762028"]}]},{"name":"National Natural Science Foundation of China","award":["PF19004P"],"award-info":[{"award-number":["PF19004P"]}]},{"name":"Guangxi Automatic Testing Technology and Instrument Key Laboratory Foundation","award":["61762028"],"award-info":[{"award-number":["61762028"]}]},{"name":"Guangxi Automatic Testing Technology and Instrument Key Laboratory Foundation","award":["PF19004P"],"award-info":[{"award-number":["PF19004P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.<\/jats:p>","DOI":"10.3390\/s22176702","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-9301","authenticated-orcid":false,"given":"Bin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3699-855X","authenticated-orcid":false,"given":"Chuan-Feng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8200-5259","authenticated-orcid":false,"given":"Shu-Qi","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electronic and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye-Hai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Liuzhou Wuling Automobile Industry Co., Ltd., Liuzhou 545000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Su","sequence":"additional","affiliation":[{"name":"Liuzhou Wuling Automobile Industry Co., Ltd., Liuzhou 545000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1007\/s10462-021-10061-9","article-title":"Deep reinforcement learning in computer vision: A comprehensive survey","volume":"55","author":"Le","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Campero-Jurado, I., M\u00e1rquez-S\u00e1nchez, S., Quintanar-G\u00f3mez, J., Rodr\u00edguez, S., and Corchado, J.M. (2020). Smart helmet 5.0 for industrial internet of things using artificial intelligence. Sensors, 20.","DOI":"10.3390\/s20216241"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Otgonbold, M.-E., Gochoo, M., Alnajjar, F., Ali, L., Tan, T.-H., Hsieh, J.-W., and Chen, P.-Y. (2022). SHEL5K: An extended dataset and benchmarking for safety helmet detection. Sensors, 22.","DOI":"10.3390\/s22062315"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16783","DOI":"10.1007\/s11042-022-12014-y","article-title":"Safety helmet wearing status detection based on improved boosted random ferns","volume":"81","author":"Yue","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1049\/ipr2.12231","article-title":"Automatic detection of safety helmet wearing based on head region location","volume":"15","author":"Gu","year":"2021","journal-title":"IET Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e311","DOI":"10.7717\/peerj-cs.311","article-title":"A deep learning-based ensemble method for helmet-wearing detection","volume":"6","author":"Fan","year":"2020","journal-title":"PeerJ Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cheng, R., He, X., Zheng, Z., and Wang, Z. (2021). Multi-scale safety helmet detection based on SAS-YOLOv3-tiny. Appl. Sci., 11.","DOI":"10.3390\/app11083652"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nan, Y., Jian-Hua, Q., Zhen, W., and Hong-Chang, W. (2022, January 15\u201317). Safety Helmet Detection Dynamic Model Based on the Critical Area Attention Mechanism. Proceedings of the 2022 7th Asia Conference on Power and Electrical Engineering (ACPEE), Hangzhou, China.","DOI":"10.1109\/ACPEE53904.2022.9783764"},{"key":"ref_9","first-page":"3181","article-title":"Helmet wearing detection method based on new feature fusion","volume":"42","year":"2021","journal-title":"Comput. Eng. Des."},{"key":"ref_10","first-page":"216","article-title":"Improved YOLOv3 Helmet Wearing Detection Method","volume":"57","year":"2021","journal-title":"J. Comput. Eng. Appl. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ben-yang, D., Xiao-chun, L., and Miao, Y. (2020, January 27\u201330). Safety helmet detection method based on YOLO v4. Proceedings of the 2020 16th International Conference on Computational Intelligence and Security (CIS), Guangxi, China.","DOI":"10.1109\/CIS52066.2020.00041"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zeng, L., Duan, X., Pan, Y., and Deng, M. (2022). Research on the algorithm of helmet-wearing detection based on the optimized yolov4. Vis. Comput., 1\u201311.","DOI":"10.1007\/s00371-022-02471-9"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gao, S., Ruan, Y., Wang, Y., Xu, W., and Zheng, M. (2022, January 24\u201326). Safety Helmet Detection based on YOLOV4-M. Proceedings of the 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA54878.2022.9844621"},{"key":"ref_14","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_16","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv, pre print."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106700","DOI":"10.1016\/j.compag.2022.106700","article-title":"A detection approach for bundled log ends using K-median clustering and improved YOLOv4-Tiny network","volume":"194","author":"Lin","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","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_19","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). European conference on computer vision. SSD: Single Shot Multibox Detector, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","unstructured":"Misra, D. (2019). Mish: A self regularized non-monotonic neural activation function. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, Z., Du, B., Zhang, M., and Liu, J. (2020, January 7\u201310). Reluplex made more practical: Leaky ReLU. Proceedings of the 2020 IEEE Symposium on Computers and communications (ISCC), Rennes, France.","DOI":"10.1109\/ISCC50000.2020.9219587"},{"key":"ref_23","first-page":"75","article-title":"Object tracking in siamese network with attention mechanism and Mish function","volume":"4","author":"Zhang","year":"2021","journal-title":"Acad. J. Comput. Inf. Sci."},{"key":"ref_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1093\/nsr\/nwx106","article-title":"A brief introduction to weakly supervised learning","volume":"5","author":"Zhou","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2848365","DOI":"10.1155\/2022\/2848365","article-title":"SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism","volume":"2022","author":"Cui","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3465055","article-title":"An attentive survey of attention models","volume":"12","author":"Chaudhari","year":"2021","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lasloum, T., Alhichri, H., Bazi, Y., and Alajlan, N. (2021). SSDAN: Multi-source semi-supervised domain adaptation network for remote sensing scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13193861"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"27899","DOI":"10.1109\/ACCESS.2019.2901599","article-title":"Sparse label smoothing regularization for person re-identification","volume":"7","author":"Ainam","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9982484","DOI":"10.1155\/2021\/9982484","article-title":"User Value Identification Based on Improved RFM Model and-Means++ Algorithm for Complex Data Analysis","volume":"2021","author":"Wu","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:27Z","timestamp":1760142207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":31,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176702"],"URL":"https:\/\/doi.org\/10.3390\/s22176702","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,5]]}}}