{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:51:47Z","timestamp":1762674707178},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"China Tobacco Henan Science and Technology Project","award":["AW202122"],"award-info":[{"award-number":["AW202122"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To address the issues of poor detection accuracy and the large number of target detection model parameters in existing AGV monocular vision location detection algorithms, this paper presents an AGV vision location method based on Gaussian saliency heuristic. The proposed method introduces a fast and accurate AGV visual detection network called GAGV-net. In the GAGV-net network, a Gaussian saliency feature extraction module is designed to enhance the network\u2019s feature extraction capability, thereby reducing the required output for model fitting. To improve the accuracy of target detection, a joint multi-scale classification and detection task header are designed at the stage of target frame regression to classification. This header utilizes target features of different scales, thereby enhancing the accuracy of target detection. Experimental results demonstrate a 12% improvement in detection accuracy and a 27.38 FPS increase in detection speed compared to existing detection methods. Moreover, the proposed detection network significantly reduces the model\u2019s size, enhances the network model\u2019s deployability on AGVs, and greatly improves detection accuracy.<\/jats:p>","DOI":"10.1186\/s13634-024-01112-8","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T18:02:02Z","timestamp":1710871322000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AGV monocular vision localization algorithm based on Gaussian saliency heuristic"],"prefix":"10.1186","volume":"2024","author":[{"given":"Heng","family":"Fu","sequence":"first","affiliation":[]},{"given":"Yakai","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Shuhua","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jianxin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Benxue","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"1112_CR1","doi-asserted-by":"crossref","unstructured":"T. Xue, P. Zeng, H. Yu, A reinforcement learning method for multi-agv scheduling in manufacturing, in 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 1557\u20131561 (2018)","DOI":"10.1109\/ICIT.2018.8352413"},{"key":"1112_CR2","volume-title":"Research and Software Implementation of Electromagnetic Guided AGV Single Machine Control","author":"Y Mao","year":"2006","unstructured":"Y. Mao, Research and Software Implementation of Electromagnetic Guided AGV Single Machine Control (Kunming University of Science and Technology, Kunming, 2006)"},{"key":"1112_CR3","volume-title":"Research on Laser Guided AGV Vehicle Control System","author":"Y Shen","year":"2007","unstructured":"Y. Shen, Research on Laser Guided AGV Vehicle Control System (Hefei University of Technology, Hefei, 2007)"},{"issue":"007","key":"1112_CR4","first-page":"30","volume":"036","author":"X Lin","year":"2019","unstructured":"X. Lin, Structure design and control strategy of magnetic navigation AGV. J. Jilin Inst. Chem. Technol. 036(007), 30\u201335 (2019)","journal-title":"J. Jilin Inst. Chem. Technol."},{"key":"1112_CR5","volume-title":"Research and Implementation of Magnetic Navigation AGV Vehicle Control System","author":"B Wang","year":"2019","unstructured":"B. Wang, Research and Implementation of Magnetic Navigation AGV Vehicle Control System (Guilin University of Electronic Technology, Guilin, 2019)"},{"key":"1112_CR6","doi-asserted-by":"crossref","unstructured":"J. Kang, J. Lee, H. Eum, C.-H. Hyun, M. Parks, An application of parameter extraction for AGV navigation based on computer vision, in 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 622\u2013626 (2013)","DOI":"10.1109\/URAI.2013.6677408"},{"key":"1112_CR7","doi-asserted-by":"crossref","unstructured":"X. Ding, D. Zhang, L. Zhang, L. Zhang, C. Zhang, B. Xu, Fault detection for automatic guided vehicles based on decision tree and LSTM, in 2021 5th International Conference on System Reliability and Safety (ICSRS), pp. 42\u201346 (2021)","DOI":"10.1109\/ICSRS53853.2021.9660624"},{"key":"1112_CR8","unstructured":"P. Kuang, Q. Zhu, G. Liu, Real-time road lane recognition using fuzzy reasoning for AGV vision system, in 2004 International Conference on Communications, Circuits and Systems (IEEE Cat. No.04EX914), pp. 989\u2013993 (2004)"},{"key":"1112_CR9","doi-asserted-by":"publisher","first-page":"119132","DOI":"10.1109\/ACCESS.2022.3220665","volume":"10","author":"D Yang","year":"2022","unstructured":"D. Yang, C. Su, H. Wu, X. Xu, X. Zhao, Shelter identification for shelter-transporting AGV based on improved target detection model YOLOv5. IEEE Access 10, 119132\u2013119139 (2022)","journal-title":"IEEE Access"},{"key":"1112_CR10","doi-asserted-by":"crossref","unstructured":"S. Liu, M. Xiong, W. Zhong, H. Xiong, Towards industrial scenario lane detection: vision-based AGV navigation methods, in 2020 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1101\u20131106 (2020)","DOI":"10.1109\/ICMA49215.2020.9233837"},{"key":"1112_CR11","doi-asserted-by":"crossref","unstructured":"J. Dong, X. Ren, S. Han, S. Luo, UAV vision aided INS\/odometer integration for land vehicle autonomous navigation, in IEEE Transactions on Vehicular Technology, pp. 4825\u20134840 (2022)","DOI":"10.1109\/TVT.2022.3151729"},{"key":"1112_CR12","doi-asserted-by":"crossref","unstructured":"L. Li, Y. -H. Liu, M. Fang, Z. Zheng, H. Tang, Vision-based intelligent forklift automatic guided vehicle (AGV), in 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 264\u2013265 (2015)","DOI":"10.1109\/CoASE.2015.7294072"},{"key":"1112_CR13","doi-asserted-by":"crossref","unstructured":"R. Girshick, \u201cFast R-CNN\u201d, in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1112_CR14","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1112_CR15","doi-asserted-by":"crossref","unstructured":"W. Liu, et al., SSD: single shot multibox detector, in ECCV (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1112_CR16","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1112_CR17","doi-asserted-by":"crossref","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137\u20131149 (2017)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"1112_CR18","doi-asserted-by":"crossref","unstructured":"R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1112_CR19","unstructured":"J. Redmon, A. Farhadi, YOLOv3: an incremental improvement. arXiv e-prints (2018)"},{"key":"1112_CR20","doi-asserted-by":"crossref","unstructured":"X. Zhu, S. Lyu, X. Wang, Q. Zhao, TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios, in 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"1112_CR21","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Doll\u00e1r, R. Girshick, \u201cMask R-CNN\u201d, in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1112_CR22","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","volume":"81","author":"V Lepetit","year":"2009","unstructured":"V. Lepetit, F. Moreno-Noguer, P. Fua, EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155\u2013166 (2009)","journal-title":"Int. J. Comput. Vis."},{"key":"1112_CR23","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in IEEE CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1112_CR24","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"28","author":"S Ren","year":"2017","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1112_CR25","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1109\/JSTARS.2022.3157648","volume":"15","author":"T Chen","year":"2022","unstructured":"T. Chen, Z. Lu, Y. Yang, Y. Zhang, B. Du, A. Plaza, A siamese network based U-net for change detection in high resolution remote sensing images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 2357\u20132369 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"1112_CR26","doi-asserted-by":"crossref","unstructured":"T. Kong, A. Yao, Y. Chen, F. Sun, HyperNet: towards accurate region proposal generation and joint object detection, in IEEE CVPR, pp. 845\u2013853 (2016)","DOI":"10.1109\/CVPR.2016.98"},{"key":"1112_CR27","doi-asserted-by":"publisher","first-page":"2557","DOI":"10.1109\/TIP.2022.3155954","volume":"31","author":"T Shi","year":"2022","unstructured":"T. Shi, N. Boutry, Y. Xu, T. G\u00e9raud, Local intensity order transformation for robust curvilinear object segmentation. IEEE Trans. Image Process. 31, 2557\u20132569 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"1112_CR28","doi-asserted-by":"crossref","unstructured":"H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y. Chen, J. Wu, UNet 3+: a full-scale connected UNet for medical image segmentation, in IEEE ICASSP, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1112_CR29","doi-asserted-by":"publisher","first-page":"6817","DOI":"10.1109\/JSTARS.2022.3198517","volume":"15","author":"W Wang","year":"2022","unstructured":"W. Wang, X. Tan, P. Zhang, X. Wang, A CBAM based multiscale transformer fusion approach for remote sensing image change detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 15, 6817\u20136825 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"1112_CR30","unstructured":"Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun, Yolox: exceeding yolo series in 2021. arXiv:2107.08430 (2021)"},{"key":"1112_CR31","doi-asserted-by":"crossref","unstructured":"W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A.C. Berg, SSD: single shot multibox detector. arXiv:1512.02325 [cs], 9905:21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1112_CR32","unstructured":"J. Redmon, A. Farhadi, Yolov3: An Incremental Improvement, CoRR, vol. abs\/1804.02767 (2018)"},{"key":"1112_CR33","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 318\u2013327 (2020)","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"1112_CR34","doi-asserted-by":"crossref","unstructured":"M. Tan, R. Pang, Q.V. Le, EfficientDet: scalable and efficient object detection, in 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10778\u201310787 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01112-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-024-01112-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01112-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T18:04:15Z","timestamp":1710871455000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-024-01112-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,19]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1112"],"URL":"https:\/\/doi.org\/10.1186\/s13634-024-01112-8","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,19]]},"assertion":[{"value":"19 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"We declare that the funding \u201cChina Tobacco Henan Science and Technology Project (AW202122)\u201d provided in this article does not affect the results or performance of this study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40"}}