{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T20:23:19Z","timestamp":1775852599961,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U1804147"],"award-info":[{"award-number":["U1804147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Innovative Scientists and Technicians Team of Henan Provincial High Education","award":["20IRTSTHN019"],"award-info":[{"award-number":["20IRTSTHN019"]}]},{"name":"Science and Technology Project of Henan Province","award":["212102210508"],"award-info":[{"award-number":["212102210508"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00371-022-02727-4","type":"journal-article","created":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T20:02:31Z","timestamp":1670184151000},"page":"6265-6277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["B-FPN SSD: an SSD algorithm based on a bidirectional feature fusion pyramid"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2157-0278","authenticated-orcid":false,"given":"Qunpo","family":"Liu","sequence":"first","affiliation":[]},{"given":"Junjia","family":"Bi","sequence":"additional","affiliation":[]},{"given":"Jingwen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuhui","family":"Bu","sequence":"additional","affiliation":[]},{"given":"Naohiko","family":"Hanajima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,4]]},"reference":[{"issue":"02","key":"2727_CR1","first-page":"37","volume":"46","author":"MR Zhu","year":"2022","unstructured":"Zhu, M.R., Niu, H.X.: Railway foreign body intrusion recognition algorithm based on improved YOLOv3 model. J. Beijing Jiaotong Univ. 46(02), 37\u201345 (2022)","journal-title":"J. Beijing Jiaotong Univ."},{"key":"2727_CR2","volume-title":"Research on Identification of Foreign Objects in Coal Belts in Coal Mines Based on Deep Learning","author":"YC Zhu","year":"2021","unstructured":"Zhu, Y.C.: Research on Identification of Foreign Objects in Coal Belts in Coal Mines Based on Deep Learning. Liaoning Technical University, Liaoning (2021)"},{"key":"2727_CR3","volume-title":"Research on Foreign Object Identification Method on Belt Conveyor Based on Deep Learning","author":"HM Zhang","year":"2020","unstructured":"Zhang, H.M.: Research on Foreign Object Identification Method on Belt Conveyor Based on Deep Learning. Anhui University of Technology, Anhui (2020)"},{"key":"2727_CR4","volume-title":"Research on Image Recognition of Foreign Objects in Coal Mine Belt Transportation Under Complex Environment","author":"ZQ Lv","year":"2020","unstructured":"Lv, Z.Q.: Research on Image Recognition of Foreign Objects in Coal Mine Belt Transportation Under Complex Environment. China University of Mining and Technology, Beijing (2020)"},{"key":"2727_CR5","unstructured":"Wu S P, Ding E J, Y X. Identification method of foreign objects in conveyor belt based on improved FPN. Safety in Coal Mines, 2019,50(12):127\u2013130."},{"key":"2727_CR6","unstructured":"Hu J H, Gao Y, Zhang H J, et al. Identification method of non-coal foreign matter in belt conveyor based on deep learning. Journal of Mine Automation, 2021, 47(06):57\u201362+90."},{"key":"2727_CR7","unstructured":"Zhang Y. Research on Traffic Target Detection Algorithm Based on YOLO-V3[D]. Anhui University of Science & Technology, 2021."},{"issue":"09","key":"2727_CR8","first-page":"56","volume":"34","author":"ZH Yuan","year":"2020","unstructured":"Yuan, Z.H., Sun, Q., Li, G.X., et al.: Automatic driving target detection based on Yolov3. J.Chongqing Univ. Technol. (Natural Sci.) 34(09), 56\u201361 (2020)","journal-title":"J.Chongqing Univ. Technol. (Natural Sci.)"},{"issue":"4","key":"2727_CR9","first-page":"438","volume":"58","author":"XY Zhang","year":"2018","unstructured":"Zhang, X.Y., Gao, H.B., Zhao, J.H.: Overview of deep learning intelligent driving methods. J Tsinghua Univ 58(4), 438\u2013444 (2018)","journal-title":"J Tsinghua Univ"},{"key":"2727_CR10","doi-asserted-by":"crossref","unstructured":"Wu H, C.Y.W.N.. Sequence Level Semantics Aggregation for Video Object Detection. IEEE, 2019.","DOI":"10.1109\/ICCV.2019.00931"},{"issue":"5","key":"2727_CR11","first-page":"8","volume":"10","author":"CH Xiong","year":"2019","unstructured":"Xiong, C.H., Lv, W.H., Wu, W.: Application and Development of Artificial Intelligence Technology for Intelligence Reconnaissance Field. Command Information System and Technology 10(5), 8\u201313 (2019)","journal-title":"Command Information System and Technology"},{"issue":"04","key":"2727_CR12","first-page":"183","volume":"41","author":"HH Li","year":"2020","unstructured":"Li, H.H., Zhou, K.P., Han, T.C.: Ship object detection based on SSD improved with CReLU and FPN. Chinese Journal of Scientific Instrument 41(04), 183\u2013190 (2020)","journal-title":"Chinese Journal of Scientific Instrument"},{"key":"2727_CR13","doi-asserted-by":"crossref","unstructured":"Zhang S, Wen L, Bian X, et al. Occlusion-aware R-CNN: Detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018:637\u2013653.","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"2727_CR14","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s11554-021-01183-y","volume":"19","author":"SD Yang","year":"2022","unstructured":"Yang, S.D., Chen, Z.H., Ma, X.M., et al.: Real-time high-precision pedestrian tracking: a detection\u2013tracking\u2013correction strategy based on improved SSD and Cascade R-CNN. J. Real-Time Image Proc. 19, 287\u2013302 (2022)","journal-title":"J. Real-Time Image Proc."},{"issue":"4","key":"2727_CR15","first-page":"985","volume":"20","author":"J Li","year":"2017","unstructured":"Li, J., Liang, X., Shen, S.M., et al.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20(4), 985\u2013996 (2017)","journal-title":"IEEE Trans. Multimedia"},{"issue":"08","key":"2727_CR16","first-page":"77","volume":"47","author":"GQ Ren","year":"2021","unstructured":"Ren, G.Q., Han, H.Y., Li, C.J., et al.: Foreign object detection in coal mine belt transportation based on Fast_YOLOv3 algorithm. Industry and Mine Automation 47(08), 77\u201383 (2021)","journal-title":"Industry and Mine Automation"},{"issue":"02","key":"2727_CR17","first-page":"1","volume":"31","author":"F Xie","year":"2022","unstructured":"Xie, F., Zhu, D.J.: Survey on Deep Learning Object Detection. Computer Systems & Applications 31(02), 1\u201312 (2022)","journal-title":"Computer Systems & Applications"},{"key":"2727_CR18","unstructured":"Bochkovskiy A, Wang C Y, Liao H Y Mark. YOLOv4: Optimal speed and accuracy of object detection[EB\/OL]. (2020\u201304\u201323) [2021\u201306\u201304]. https:\/\/arxiv.org\/abs\/2004.10934."},{"issue":"07","key":"2727_CR19","first-page":"1981","volume":"43","author":"AQ Huo","year":"2022","unstructured":"Huo, A.Q., Yang, Y.Y., Xie, G.K.: Vehicle target detection based on improved YOLOv3 algorithm. COMPUTER ENGINEERING AND DESIGN. 43(07), 1981\u20131989 (2022)","journal-title":"COMPUTER ENGINEERING AND DESIGN."},{"issue":"08","key":"2727_CR20","first-page":"77","volume":"47","author":"JY Du","year":"2021","unstructured":"Du, J.Y., Chen, R., Hao, L., et al.: Coal mine belt conveyor foreign object detection. Industry and Mine Automation 47(08), 77\u201383 (2021)","journal-title":"Industry and Mine Automation"},{"key":"2727_CR21","unstructured":"Redmon J, Farhadi A. YOLOv3: An incremental improvement \u2225 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA: IEEE, 2017: 6517\u20136525."},{"key":"2727_CR22","unstructured":"Hao S, Zhang X, Ma X, et al. Foreign object detection in coal mine conveyor belt based on CBAM-YOLOv5. Journal of China Coal Society, 2021: 1\u201311."},{"key":"2727_CR23","first-page":"3","volume":"11211","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: Convolutional Block Attention Module. European conference on computer vision 11211, 3\u201319 (2018)","journal-title":"European conference on computer vision"},{"key":"2727_CR24","unstructured":"GLENN JOCHER, et al. YOLOv5[EB\/OL]. https:\/\/github.com\/ultralytics\/yolov5, 2021. 9"},{"key":"2727_CR25","doi-asserted-by":"publisher","first-page":"5134","DOI":"10.1109\/TIP.2022.3193288","volume":"31","author":"W Tang","year":"2022","unstructured":"Tang, W., Fazhi He, Yu., Liu, et al.: MA TR: Multimodal Medical Image Fusion via Multiscale Adaptive Transformer. IEEE Trans. Image Process. 31, 5134\u20135149 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"2727_CR26","doi-asserted-by":"crossref","unstructured":"Behnood Rasti, Pedram Ghamisi. Remote sensing image classification using subspace sensor fusion. Information Fusion. 2020,121\u2013130.","DOI":"10.1016\/j.inffus.2020.07.002"},{"key":"2727_CR27","doi-asserted-by":"crossref","unstructured":"Wei Tang, Fazhi He, Yu Liu, et al. YDTR: Infrared and Visible Image Fusion via Y -shape Dynamic Transformer. IEEE Transactions on Multimedia. 2022.","DOI":"10.1109\/TMM.2022.3192661"},{"key":"2727_CR28","unstructured":"Chenxing Xia, Yanguang Sun, Xiuju Gao, et al. DMINet: dense multi-scale inference network for salient object detection. The Visual Computer. 2022."},{"key":"2727_CR29","doi-asserted-by":"crossref","unstructured":"Pengfei Wang, Minglian Wang, Dongzhi He. Multi-scale feature pyramid and multi-branch neural network for person re-identification. The Visual Computer. 2022","DOI":"10.1007\/s00371-022-02653-5"},{"key":"2727_CR30","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D Q, Feng J S. Coordinate Attention for Efficient Mobile Network Design\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. NJ:IEEE, 2021:13713\u201313722.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"2727_CR31","doi-asserted-by":"crossref","unstructured":"Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection\/\/ Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017:936\u2013944.","DOI":"10.1109\/CVPR.2017.106"},{"issue":"05","key":"2727_CR32","first-page":"18","volume":"37","author":"XC Chen","year":"2021","unstructured":"Chen, X.C.: Improved bounding box regression loss function based on smoothL1. COLLEGE MATHEMATICS 37(05), 18\u201323 (2021)","journal-title":"COLLEGE MATHEMATICS"},{"key":"2727_CR33","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector. European Conference on Computer vision, 2016: 21 -37.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"6","key":"2727_CR34","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 91\u201399 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2727_CR35","doi-asserted-by":"crossref","unstructured":"Wang, C., A. Bochkovskiy and H.M. Liao, Scaled-YOLOv4:Scaling Cross Stage Partial Network. IEEE Conference on Computer Vision and Pattern Recognition, 2020.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"2727_CR36","unstructured":"Ge, Z., et al.: YOLOX: Exceeding YOLO Series in 2021. IEEE Conference on Computer Vision and Pattern Recognition, 2021."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02727-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02727-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02727-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T12:06:25Z","timestamp":1699531585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02727-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,4]]},"references-count":36,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2727"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02727-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,4]]},"assertion":[{"value":"8 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}