{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T05:41:37Z","timestamp":1673761297743},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"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":[[2022,1]]},"DOI":"10.1007\/s00521-021-06607-1","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T06:02:54Z","timestamp":1635919374000},"page":"1033-1045","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EFLDet: enhanced feature learning for object detection"],"prefix":"10.1007","volume":"34","author":[{"given":"Yongwei","family":"Liao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guipeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenguo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"6607_CR1","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2018","unstructured":"Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietik\u00e4 M (2018) Deep learning for generic object detection: a survey. Int J Comput Vis 128:261\u2013318","journal-title":"Int J Comput Vis"},{"key":"6607_CR2","unstructured":"Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. IEEE Trans Pattern Anal Mach Intell"},{"key":"6607_CR3","doi-asserted-by":"crossref","unstructured":"Zaidi SSA, Ansari MS, Aslam A, Kanwal N, Asghar M, Lee B (2021) A survey of modern deep learning based object detection models. IET Comput Vis","DOI":"10.1016\/j.dsp.2022.103514"},{"key":"6607_CR4","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497"},{"key":"6607_CR5","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, pp 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"6607_CR6","doi-asserted-by":"crossref","unstructured":"Singh B, Davis LS (2018) An analysis of scale invariance in object detection-snip. IEEE Int Conf Comput Vis","DOI":"10.1109\/CVPR.2018.00377"},{"key":"6607_CR7","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: fully convolutional one-stage object detection. IEEE Conf Comput Vis Pattern Recogn","DOI":"10.1109\/ICCV.2019.00972"},{"key":"6607_CR8","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2020) Fcos: a simple and strong anchor-free object detector. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2020.3032166"},{"key":"6607_CR9","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"6607_CR10","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"6607_CR11","unstructured":"Ze\u00a0Y, Liu S, Hu H, Wang L, Lin S (2019) Reppoints: point set representation for object detection. In: IEEE international conference on computer vision"},{"key":"6607_CR12","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"6607_CR13","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhang H, Tang J, Wang M, Hua X, Sun Q (2020) Feature pyramid transformer. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-58604-1_20"},{"key":"6607_CR14","doi-asserted-by":"crossref","unstructured":"Sun P, Zhang R, Yi J, Kong T, Xu C, Zhan W, Tomizuka M, Li L, Yuan Z, Wang C, Luo P (2021) Sparse r-cnn: end-to-end object detection with learnable proposals. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"6607_CR15","unstructured":"Sun P, Jiang Y, Xie E, Yuan Z, Wang C (2020) and Ping Luo. Towards end-to-end one-stage object detection. arix, Onenet"},{"key":"6607_CR16","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on machine learning"},{"key":"6607_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6607_CR18","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105\u20136114. PMLR"},{"key":"6607_CR19","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.106"},{"key":"6607_CR20","unstructured":"Liu S, Di H, Wang Y (2019) Learning spatial fusion for single-shot object detection. arix"},{"key":"6607_CR21","doi-asserted-by":"crossref","unstructured":"Wu Y, Chen Y, Lu\u00a0Y, Liu Z, Wang L, Li H, Fu Y (2020) Rethinking classification and localization for object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"6607_CR22","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"6607_CR23","doi-asserted-by":"crossref","unstructured":"Gao S-H, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr Philip (2021) Res2net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"6607_CR24","unstructured":"Zhang H, Wu C, Zhang Z, Yi\u00a0Z, Zhang Z, Lin H, Sun Y, He T, Mueller J, Manmatha R et\u00a0al (2020) Resnest: split-attention networks. arXiv preprint arXiv:2004.08955"},{"key":"6607_CR25","first-page":"1","volume":"401","author":"X Wu","year":"2018","unstructured":"Wu X, Zhang D, Zhu J, Hoi SCH (2018) Single-shot bidirectional pyramid networks for high-quality object detection. Assoc Adv Artif Intell 401:1\u20139","journal-title":"Assoc Adv Artif Intell"},{"key":"6607_CR26","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"6607_CR27","doi-asserted-by":"crossref","unstructured":"Guo C, Fan B, Zhang Q, Xiang S, Pan C (2020) Augfpn: improving multi-scale feature learning for object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"6607_CR28","doi-asserted-by":"crossref","unstructured":"Wang T, Zhang X, Sun J (2021) Implicit feature pyramid network for object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/ICCV48922.2021.00276"},{"key":"6607_CR29","unstructured":"Zhang H, Chang H, Ma B, Shan S, Chen X (2019) Cascade retinanet: maintaining consistency for single-stage object detection. In: British machine vision conference"},{"key":"6607_CR30","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"6607_CR31","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Yi\u00a0L, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"key":"6607_CR32","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"6607_CR33","unstructured":"Howard AG, Zhu M, Bo\u00a0C, Kalenichenko D (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. In: IEEE conference on computer vision and pattern recognition"},{"key":"6607_CR34","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6607_CR35","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Ch G (2019)Searching for mobilenetv3. In: IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2019.00140"},{"key":"6607_CR36","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00716"},{"key":"6607_CR37","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T (2019) Shufflenet v2: practical guidelines for efficient cnn architecture design. IEEE international conference on computer vision","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"6607_CR38","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: British machine vision conference","DOI":"10.5244\/C.30.87"},{"key":"6607_CR39","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"6607_CR40","unstructured":"Chen Y, Li J, Xiao H, Jin X, Yan S, Feng J (2017) Dual path networks. arXiv preprint arXiv:1707.01629"},{"key":"6607_CR41","doi-asserted-by":"crossref","unstructured":"Hu J, Li\u00a0S, Sun G (2017) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6607_CR42","doi-asserted-by":"crossref","unstructured":"Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 510\u2013519","DOI":"10.1109\/CVPR.2019.00060"},{"key":"6607_CR43","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"6607_CR44","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"6607_CR45","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"6607_CR46","unstructured":"Qilong W, Wu\u00a0B, Pengfei Z, Li\u00a0P, Wangmeng Z, Hu\u00a0Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition"},{"key":"6607_CR47","doi-asserted-by":"crossref","unstructured":"Misra D, Nalamada T et al (2020) Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 510\u2013519","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"6607_CR48","doi-asserted-by":"crossref","unstructured":"Liu S, Di\u00a0H, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"6607_CR49","doi-asserted-by":"crossref","unstructured":"Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. In: IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2019.00615"},{"key":"6607_CR50","doi-asserted-by":"crossref","unstructured":"Du X, Lin T-Y, Jin P, Ghiasi G, Tan M, Cui Y, Le QV, Song X (2020) Spinenet: learning scale-permuted backbone for recognition and localization. In: IEEE conference on computer vision and pattern recognition 2020","DOI":"10.1109\/CVPR42600.2020.01161"},{"key":"6607_CR51","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang Y, Wang S, Liang T, Zhao Q, Tang Z, Ling H (2020) Cbnet: a novel composite backbone network architecture for object detection. In: IEEE conference on computer vision and pattern recognition","DOI":"10.1609\/aaai.v34i07.6834"},{"key":"6607_CR52","doi-asserted-by":"crossref","unstructured":"Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2018) Detnet: a backbone network for object detection. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"6607_CR53","unstructured":"Luo Y, Cao X, Zhang J, Cao X, Guo J, Shen H, Wang T (2020) and Qi Feng. Enhancing channel information for object detection. Arix, Ce-fpn"},{"key":"6607_CR54","unstructured":"Kong T, Sun F, Liu H, Jiang Y, Shi J (2019) Consistent optimization for single-shot object detection. In: IEEE conference on computer vision and pattern recognition"},{"key":"6607_CR55","unstructured":"Li Z, Peng C, Gang Y, Zhang X, Deng Y (2017) and Jian Sun. In defense of two-stage object detector. arix, Light-head r-cnn"},{"key":"6607_CR56","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"6607_CR57","doi-asserted-by":"crossref","unstructured":"Jiang B, Luo R, Mao J, Xiao T, Jiang Y (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"6607_CR58","unstructured":"Huang Z, Huang L, Gong Y, Huang C, Wang X (2019) Path aggregation network for instance segmentation. In: IEEE conference on computer vision and pattern recognition"},{"key":"6607_CR59","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06607-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06607-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06607-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T10:54:19Z","timestamp":1673693659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06607-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6607"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06607-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,3]]},"assertion":[{"value":"15 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}