{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:58:43Z","timestamp":1789689523255,"version":"4.0.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05150-9","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:03:31Z","timestamp":1594371811000},"page":"2769-2781","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Adaptive feature fusion with attention mechanism for multi-scale target detection"],"prefix":"10.1007","volume":"33","author":[{"given":"Moran","family":"Ju","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangning","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6425-6433","authenticated-orcid":false,"given":"Haibo","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"5150_CR1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of NIPS, pp 1097\u20131105"},{"key":"5150_CR2","unstructured":"Liu L, Ouyang W, Wang X, Fieguth P, Liu X, Pietik\u00e4inen M (2018) Deep learning for generic object detection: a survey. arXiv preprint https:\/\/arxiv.org\/abs\/1809.02165v4"},{"key":"5150_CR3","doi-asserted-by":"publisher","first-page":"7361","DOI":"10.1007\/s00521-018-3579-x","volume":"31","author":"H Zhang","year":"2019","unstructured":"Zhang H, Ji Y, Huang W et al (2019) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl 31:7361\u20137380","journal-title":"Neural Comput Appl"},{"key":"5150_CR4","doi-asserted-by":"crossref","unstructured":"Levi G, Hassner T (2015) Age and gender classification using convolution neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 34\u201342","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"5150_CR5","unstructured":"Attia A, Dayan S (2018) Detecting and counting tiny faces. arXiv preprint https:\/\/arxiv.org\/abs\/1801.06504"},{"key":"5150_CR6","doi-asserted-by":"crossref","unstructured":"Makantasis K, Doulamis A, Doulamis N, Psychas K (2016) Deep learning based human behavior recognition in industrial workflows. In: Proceedings of the international conference on image processing, pp 1609\u20131613","DOI":"10.1109\/ICIP.2016.7532630"},{"key":"5150_CR7","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TMM.2015.2477242","volume":"17","author":"M Hasan","year":"2015","unstructured":"Hasan M, Roy-Chowdhury AK (2015) A continuous learning framework for activity recognition using deep hybrid feature models. IEEE Trans Multimed 17:1909\u20131922","journal-title":"IEEE Trans Multimed"},{"key":"5150_CR8","doi-asserted-by":"crossref","unstructured":"Zhou Y, Liu L, Shao L, Mellor M (2016) DAVE: a unified framework for fast vehicle detection and annotation. arXiv preprint https:\/\/arxiv.org\/abs\/1607.04564","DOI":"10.1007\/978-3-319-46475-6_18"},{"key":"5150_CR9","doi-asserted-by":"crossref","unstructured":"Wang L, Lu Y, Wang H, Zheng Y, Ye H, Xue X (2017) evolving boxes for fast vehicle detection. arXiv preprint https:\/\/arxiv.org\/abs\/1702.00254","DOI":"10.1109\/ICME.2017.8019461"},{"key":"5150_CR10","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1109\/LGRS.2015.2439517","volume":"12","author":"K Liu","year":"2015","unstructured":"Liu K, Mattyus G (2015) Fast multiclass vehicle detection on aerial images. IEEE Geosci Remote Sens Lett 12:1938\u20131942","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5150_CR11","doi-asserted-by":"crossref","unstructured":"Chen X, Ma H, Wan J, Li B, Xia T (2016) Multi-view 3D object detection network for autonomous driving. arXiv preprint https:\/\/arxiv.org\/abs\/1611.07759","DOI":"10.1109\/CVPR.2017.691"},{"key":"5150_CR12","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1177\/0037549717709932","volume":"93","author":"A U\u00e7ar","year":"2017","unstructured":"U\u00e7ar A, Demir Y, G\u00fczeli C (2017) Object recognition and detection with deep learning for autonomous driving applications. Simulation 93:759\u2013769","journal-title":"Simulation"},{"key":"5150_CR13","doi-asserted-by":"crossref","unstructured":"Nguyen-Meidine LT, Granger E, Kiran M, Blais-Morin LA (2018) A comparison of cnn-based face and head detectors for real-time video surveillance applications. arXiv preprint https:\/\/arxiv.org\/abs\/1809.03336","DOI":"10.1109\/IPTA.2017.8310113"},{"key":"5150_CR14","doi-asserted-by":"crossref","unstructured":"Yu R, Wang H, Davis LS (2018) ReMotENet: efficient relevant motion event detection for large-scale home surveillance videos. arXiv preprint https:\/\/arxiv.org\/abs\/1801.02031","DOI":"10.1109\/WACV.2018.00183"},{"key":"5150_CR15","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5150_CR16","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. In: Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv preprint https:\/\/arxiv.org\/abs\/1804.02767"},{"key":"5150_CR17","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot multi box detector. In: Proceedings of the European conference on computer vision, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5150_CR18","unstructured":"Fu CY, Liu W, Ranga A et al (2017) DSSD: deconvolutional single shot detector. In: Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv preprint https:\/\/arxiv.org\/abs\/1701.06659"},{"key":"5150_CR19","doi-asserted-by":"crossref","unstructured":"Lin T, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"5150_CR20","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R et al (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.207"},{"key":"5150_CR21","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak JY, Sadeghian A, Reid, I, Savarese S (2019). Generalized intersection over union: a metric and a loss for bounding box regression. arXiv preprint https:\/\/arxiv.org\/abs\/1902.09630","DOI":"10.1109\/CVPR.2019.00075"},{"key":"5150_CR22","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2017","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42:318\u2013327","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5150_CR23","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2014.81"},{"key":"5150_CR24","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision and pattern recognition, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"5150_CR25","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick RB et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5150_CR26","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P et al (2018) Mask R-CNN. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20131","DOI":"10.1109\/ICCV.2017.322"},{"key":"5150_CR27","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00644"},{"key":"5150_CR28","unstructured":"Van SKEA, Uijlings JRR, Gevers T et al (2011) Segmentation as selective search for object recognition. In: Proceedings of the 2011 international conference on computer vision, pp 1879\u20131886"},{"key":"5150_CR29","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R et al (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"5150_CR30","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"key":"5150_CR31","unstructured":"Zhou X, Wang D, Philipp K (2019) Objects as points. arXiv preprint https:\/\/arxiv.org\/abs\/1904.07850"},{"key":"5150_CR32","doi-asserted-by":"crossref","unstructured":"Law H, Deng J (2018) Cornernet: detecting objects as paired key points, In: Proceedings of European conference on computer vision, pp 765\u2013781","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"5150_CR33","doi-asserted-by":"publisher","first-page":"6549","DOI":"10.1007\/s00521-017-3076-7","volume":"31","author":"XL Jia","year":"2019","unstructured":"Jia XL, Liu Y (2019) An enhanced SSD with feature fusion and visual reasoning for object detection. Neural Comput Appl 31:6549\u20136558","journal-title":"Neural Comput Appl"},{"key":"5150_CR34","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42:2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5150_CR35","unstructured":"Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. arXiv preprint https:\/\/arxiv.org\/abs\/1608.03983"},{"key":"5150_CR36","unstructured":"Li Y, He K, Sun J et al (2016) R-FCN: object detection via region-based fully convolutional networks. In: Proceedings of NIPS."},{"key":"5150_CR37","doi-asserted-by":"crossref","unstructured":"Dai J et al (2017) Deformable convolutional networks. In: Proceedings of the European conference on computer vision","DOI":"10.1109\/ICCV.2017.89"},{"key":"5150_CR38","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European conference on computer vision","DOI":"10.1007\/978-3-030-01252-6_24"},{"issue":"7","key":"5150_CR39","doi-asserted-by":"publisher","first-page":"703005","DOI":"10.3788\/IRLA201847.0703005","volume":"47","author":"TS Wu","year":"2018","unstructured":"Wu TS, Zhang ZJ, Liu YP et al (2018) A light weight small object detection algorithm based on improved SSD. Infrared Laser Eng 47(7):703005","journal-title":"Infrared Laser Eng"},{"key":"5150_CR40","doi-asserted-by":"crossref","unstructured":"Zhang H, Li D, Ji Y, Zhou H, Wu W and Liu K (2019) Towards new retail: a benchmark dataset for smart unmanned vending machines In: IEEE transactions on industrial informatics","DOI":"10.1109\/TII.2019.2954956"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05150-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05150-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05150-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T20:19:29Z","timestamp":1625861969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05150-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,10]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5150"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05150-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,10]]},"assertion":[{"value":"28 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}