{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T14:31:17Z","timestamp":1769178677090,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2019YFC1711200"],"award-info":[{"award-number":["2019YFC1711200"]}]},{"name":"the National Key R&amp;D Program of China","award":["2019JZZY020113"],"award-info":[{"award-number":["2019JZZY020113"]}]},{"name":"the National Key R&amp;D Program of China","award":["2019JZZY010732"],"award-info":[{"award-number":["2019JZZY010732"]}]},{"name":"the Shandong Key Laboratory of Computer Networks open project","award":["SKLCN-2020-08"],"award-info":[{"award-number":["SKLCN-2020-08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Detection of human lower body provides an implementation idea for the automatic tracking and accurate relocation of automatic vehicles. Based on traditional SSD and ResNet, this paper proposes an improved detection algorithm R-SSD for human lower body detection, which utilizes ResNet50 instead of VGG16 to improve the feature extraction level of the model. According to the application of acquisition equipment, the model input resolution is increased to 448 \u00d7 448 and the model detection range is expanded. Six feature maps of the updated resolution network are selected for detection and the lower body image dataset is clustered into five categories for aspect ratio, which are evenly distributed to each feature detection map. The experimental results show that the model R-SSD detection accuracy after training reaches 85.1% mAP. Compared with the original SSD, the detection accuracy is improved by 7% mAP. The detection confidence in practical application reaches more than 99%, which lays the foundation for subsequent tracking and relocation for automatic vehicles.<\/jats:p>","DOI":"10.3390\/s22052008","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Detection of Lower Body for AGV Based on SSD Algorithm with ResNet"],"prefix":"10.3390","volume":"22","author":[{"given":"Xinbiao","family":"Gao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"},{"name":"Shandong Alesmart Intelligent Technology Co., Ltd., Jinan 250061, China"},{"name":"National Experimental Teaching Demonstration Center of Mechanical Engineering, School of Mechanical Engineering, Shandong University, Jinan 250061, China"}]},{"given":"Junhua","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"}]},{"given":"Chuan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"},{"name":"Shandong Institute of Industrial Technology, Jinan 250061, China"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"}]},{"given":"Panling","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0646-6154","authenticated-orcid":false,"given":"Jianxin","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shandong University, Jinan 250061, China"},{"name":"Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Ministry of Education, Jinan 250061, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Peter, J.D., Fernandes, S.L., and Alavi, A.H. (2021). A Mobile-Based Framework for Detecting Objects Using SSD-MobileNet in Indoor Environment. Intelligence in Big Data Technologies\u2014Beyond the Hype, Springer.","DOI":"10.1007\/978-981-15-5285-4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.pce.2018.12.001","article-title":"An Automatic Traffic Density Estimation Using Single Shot Detection (SSD) and MobileNet-SSD","volume":"110","author":"Biswas","year":"2019","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gupta, P., Sharma, V., and Varma, S. (2021). People Detection and Counting Using YOLOv3 and SSD Models. Mater. Today Proc.","DOI":"10.1016\/j.matpr.2020.11.562"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104346","DOI":"10.1016\/j.engappai.2021.104346","article-title":"Human Flow Recognition Using Deep Networks and Vision Methods","volume":"104","author":"Zimoch","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107226","DOI":"10.1016\/j.compeleceng.2021.107226","article-title":"IoT-Based Crowd Monitoring System: Using SSD with Transfer Learning","volume":"93","author":"Ahmed","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.procs.2020.04.283","article-title":"Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector","volume":"171","author":"Kumar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cviu.2019.01.006","article-title":"Registration-Free Face-SSD: Single Shot Analysis of Smiles, Facial Attributes, and Affect in the Wild","volume":"182","author":"Jang","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102692","DOI":"10.1016\/j.scs.2020.102692","article-title":"SSDMNV2: A Real Time DNN-Based Face Mask Detection System Using Single Shot Multibox Detector and MobileNetV2","volume":"66","author":"Nagrath","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_12","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NE, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Biswas, D., Su, H., Wang, C., Blankenship, J., and Stevanovic, A. (2017). An Automatic Car Counting System Using Over Feat Framework. Sensors, 17.","DOI":"10.3390\/s17071535"},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"24344","DOI":"10.1109\/ACCESS.2020.2971026","article-title":"DF-SSD: An Improved SSD Object Detection Algorithm Based on DenseNet and Feature Fusion","volume":"8","author":"Zhai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hao, G., Yingkun, Y., and Yi, Q. (2019, January 24\u201326). General Target Detection Method Based on Improved SSD. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785733"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_25","unstructured":"(2022, February 15). GitHub\u2014Tzutalin\/LabelImg: LabelImg Is a Graphical Image Annotation Tool and Label Object Bounding Boxes in Images. Available online: https:\/\/github.com\/tzutalin\/labelImg."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2008\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:31:59Z","timestamp":1760135519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22052008"],"URL":"https:\/\/doi.org\/10.3390\/s22052008","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}