{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:36:24Z","timestamp":1769160984301,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51865054"],"award-info":[{"award-number":["51865054"]}]},{"name":"National Natural Science Foundation of China","award":["2022D01A117"],"award-info":[{"award-number":["2022D01A117"]}]},{"name":"Xinjiang Uygur Autonomous Region Foundation Project","award":["51865054"],"award-info":[{"award-number":["51865054"]}]},{"name":"Xinjiang Uygur Autonomous Region Foundation Project","award":["2022D01A117"],"award-info":[{"award-number":["2022D01A117"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features\u2019 interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same.<\/jats:p>","DOI":"10.3390\/s22207836","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"7836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Fan","family":"Sun","sequence":"first","affiliation":[{"name":"College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China"}]},{"given":"Xiangfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China"}]},{"given":"Yunzhong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China"}]},{"given":"Hong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A multi-objective prohibited item identification algorithm in the X-ray security scene","volume":"5","author":"Cao","year":"2021","journal-title":"Laser Optoelectron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Alex","year":"2017","journal-title":"Commun. ACM"},{"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 International Conference on Computer Vision, 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","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_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201327). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_8","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_9","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H. (2022). Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_11","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lin, T., 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_13","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":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_15","first-page":"2999","article-title":"Focal loss for dense object detection","volume":"99","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_17","first-page":"35","article-title":"Survey on Deep Neural Network Image Target Detection Algorithms","volume":"7","author":"Fu","year":"2022","journal-title":"Comput. Appl. Syst."},{"key":"ref_18","first-page":"217","article-title":"X-Ray Object Detection Based on Pyramid Convolution and Strip Pooling","volume":"4","author":"Qiao","year":"2022","journal-title":"Prog. Laser Optoelectron."},{"key":"ref_19","first-page":"187","article-title":"Research on YOLO Algorithm in Abnormal Security Images","volume":"56","author":"Zhang","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van, D., 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.37188\/CO.2021-0078","article-title":"Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning","volume":"14","author":"Wu","year":"2021","journal-title":"China Opt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TIFS.2018.2812196","article-title":"Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery","volume":"13","author":"Akcay","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_23","unstructured":"Galvez, R., Dadios, E., Bandala, A., and Vicerra, R.R.P. (December, January 29). Threat object classification in X-ray images using transfer learning. Proceedings of the IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115136","DOI":"10.1016\/j.compstruct.2021.115136","article-title":"A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures","volume":"284","author":"Gong","year":"2022","journal-title":"Compos. Struct."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hassan, T., Bettayeb, M., Ak\u00e7ay, S., Khan, S., Bennamoun, M., and Werghi, N. (2020, January 25\u201328). Detecting prohibited items in X-ray images: A contour proposal learning approach. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Online.","DOI":"10.1109\/ICIP40778.2020.9190711"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Tao, R., Wei, Y., Jiang, X., Li, H., Qin, H., Wang, J., 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, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01074"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sigman, J.B., Spell, G.P., Liang, K.J., and Carin, L. (2020, January 26). Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images. Proceedings of the Anomaly Detection and Imaging with X-Rays (ADIX), Online.","DOI":"10.1117\/12.2558542"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 20\u201325). You Only Look One-level Feature. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_31","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Chapter 53\u2014Visualizing and understanding convolutional networks","volume":"Volume 8689","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014: 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). Cbam: Convolutional Block Attention Module, Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, J., Ma, X., Sansom, A., Mcguire, M., and Fu, S. (2020, January 6\u201310). Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), Online.","DOI":"10.1109\/ICME46284.2020.9102906"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective Kernel Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_38","unstructured":"Zhang, H., Zu, K., Lu, J., Zou, Y., and Meng, D. (2021). Epsanet: An efficient pyramid squeeze attention block on convolutional neural network. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ioannou, Y., Robertson, D., Cipolla, R., and Criminisi, A. (2017, January 21\u201326). Deep roots: Improving cnn efficiency with hierarchical filter groups. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.633"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019, January 15\u201320). Adaptive NMS: Refining Pedestrian Detection in a Crowd. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00662"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Soft-NMS\u2014Improving Object Detection with One Line of Code. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:41Z","timestamp":1760144081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,15]]},"references-count":41,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22207836"],"URL":"https:\/\/doi.org\/10.3390\/s22207836","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,15]]}}}