{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:22:43Z","timestamp":1767374563602,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Provincial Agricultural Basic Research Joint Special Project","award":["202101BD070001-042"],"award-info":[{"award-number":["202101BD070001-042"]}]},{"name":"Yunnan Ten-Tousand Talents Program","award":["202101BD070001-042"],"award-info":[{"award-number":["202101BD070001-042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problem of low recall rate in the detection of prohibited items in X-ray images due to the severe object occlusion and complex background, an X-ray prohibited item detection network, ScanGuard-YOLO, based on the YOLOv5 architecture, is proposed to effectively improve the model\u2019s recall rate and the comprehensive metric F1 score. Firstly, the RFB-s module was added to the end part of the backbone, and dilated convolution was used to increase the receptive field of the backbone network to better capture global features. In the neck section, the efficient RepGFPN module was employed to fuse multiscale information from the backbone output. This aimed to capture details and contextual information at various scales, thereby enhancing the model\u2019s understanding and representation capability of the object. Secondly, a novel detection head was introduced to unify scale-awareness, spatial-awareness, and task-awareness altogether, which significantly improved the representation ability of the object detection heads. Finally, the bounding box regression loss function was defined as the WIOUv3 loss, effectively balancing the contribution of low-quality and high-quality samples to the loss. ScanGuard-YOLO was tested on OPIXray and HiXray datasets, showing significant improvements compared to the baseline model. The mean average precision (mAP@0.5) increased by 2.3% and 1.6%, the recall rate improved by 4.5% and 2%, and the F1 score increased by 2.3% and 1%, respectively. The experimental results demonstrate that ScanGuard-YOLO effectively enhances the detection capability of prohibited items in complex backgrounds and exhibits broad prospects for application.<\/jats:p>","DOI":"10.3390\/s24010102","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:42:30Z","timestamp":1703450550000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["ScanGuard-YOLO: Enhancing X-ray Prohibited Item Detection with Significant Performance Gains"],"prefix":"10.3390","volume":"24","author":[{"given":"Xianning","family":"Huang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5991-661X","authenticated-orcid":false,"given":"Yaping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"ref_1","first-page":"2675","article-title":"An Algorithm for Detection of Prohibited Items in X-ray Images Based on Improved YOLOv4","volume":"42","author":"Mu","year":"2021","journal-title":"Acta Armamentarii"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding Convolution for Semantic Segmentation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_3","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_4","first-page":"427","article-title":"X-ray Detection of Prohibited Items Based on Improved YOLOX","volume":"45","author":"Wu","year":"2023","journal-title":"Infrared Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u20134). 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_6","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"0810012","DOI":"10.3788\/LOP202158.0810012","article-title":"Dangerous Goods Detection Based on Multi-Scale Feature Fusion in Security Images","volume":"58","author":"Wang","year":"2021","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_8","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_9","first-page":"193","article-title":"Improved YOLOv7 X-Ray Image Real-Time Detection of Prohibited Items","volume":"59","author":"Song","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_10","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\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"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xiang, N., Gong, Z., Xu, Y., and Xiong, L. (2023). Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-Ray Contraband Detection. Electronics, 12.","DOI":"10.3390\/electronics12051179"},{"key":"ref_13","unstructured":"Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, B., Zhang, L., Wen, L., Liu, X., and Wu, Y. (2021, January 10\u201317). Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00536"},{"key":"ref_15","unstructured":"Jocher, G., Chaurasia, A., and Borovec, J. (2023, October 22). YOLOv5 by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_16","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_17","unstructured":"Xu, X., Jiang, Y., Chen, W., Huang, Y., Zhang, Y., and Sun, X. (2023). DAMO-YOLO: A Report on Real-Time Object Detection Design. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 20\u201325). Dynamic Head: Unifying Object Detection Heads with Attentions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"ref_19","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_20","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, October 22). YOLOv8 by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wei, Y., Tao, R., Wu, Z., Ma, Y., Zhang, L., and Liu, X. (2020, January 12\u201316). Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-Occlusion Attention Module. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413828"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tao, R., Wei, Y., Jiang, X., Li, H., Qin, H., Wang, J., Ma, Y., Zhang, L., and Liu, X. (2021, January 10\u201317). Towards Real-World X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01074"},{"key":"ref_23","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 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_25","unstructured":"Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., and Chu, X. (2023). YOLOv6 v3.0: A Full-Scale Reloading. arXiv."},{"key":"ref_26","unstructured":"Zhuang, J., Qin, Z., Yu, H., and Chen, X. (2023). Task-Specific Context Decoupling for Object Detection. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable Convnets v2: More Deformable, Better Results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_28","first-page":"351","article-title":"Dynamic ReLU","volume":"Volume 12364","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and Efficient IOU Loss for Accurate Bounding Box Regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"34","author":"Zheng","year":"2020","journal-title":"AAAI"},{"key":"ref_32","first-page":"20230","article-title":"Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression","volume":"34","author":"He","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:41:23Z","timestamp":1760132483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010102"],"URL":"https:\/\/doi.org\/10.3390\/s24010102","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,24]]}}}