{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:19:43Z","timestamp":1776680383375,"version":"3.51.2"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Science and Technology Cooperation Project of The Xinjiang Production","award":["2019BC008"],"award-info":[{"award-number":["2019BC008"]}]},{"name":"Science and Technology Project of Tarim University","award":["TDZKZD202104"],"award-info":[{"award-number":["TDZKZD202104"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11227-022-04596-z","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T16:05:41Z","timestamp":1654272341000},"page":"18209-18224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A lightweight network for vehicle detection based on embedded system"],"prefix":"10.1007","volume":"78","author":[{"given":"Huanhuan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuantao","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"key":"4596_CR1","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"4596_CR2","doi-asserted-by":"crossref","unstructured":"Min R, Quan H, Cui Z, Cao Z, Pi Y, Xu, Z (2019) SAR Target detection using AdaBoost via GPU acceleration. In: IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium pp 1180\u20131183. IEEE","DOI":"10.1109\/IGARSS.2019.8899296"},{"issue":"12","key":"4596_CR3","doi-asserted-by":"publisher","first-page":"3992","DOI":"10.3390\/s21123992","volume":"21","author":"J Mendez","year":"2021","unstructured":"Mendez J, Molina M, Rodriguez N, Cuellar MP, Morales DP (2021) Camera-LiDAR multi-level sensor fusion for target detection at the network edge. Sensors 21(12):3992","journal-title":"Sensors"},{"issue":"5","key":"4596_CR4","doi-asserted-by":"publisher","first-page":"2746","DOI":"10.1007\/s11694-020-00520-2","volume":"14","author":"S Srivastava","year":"2020","unstructured":"Srivastava S, Vani B, Sadistap S (2020) Machine-vision based handheld embedded system to extract quality parameters of citrus cultivars. J Food Measure Char 14(5):2746\u20132759","journal-title":"J Food Measure Char"},{"issue":"4","key":"4596_CR5","doi-asserted-by":"publisher","first-page":"0415006","DOI":"10.3788\/AOS201939.0415006","volume":"39","author":"C Jiahua","year":"2019","unstructured":"Jiahua C, Yunzhou Z, Zheng W, Jiwei L (2019) Light-weight object detection networks for embedded platform. Acta Optica Sinica 39(4):0415006","journal-title":"Acta Optica Sinica"},{"key":"4596_CR6","unstructured":"Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. Adv Neural Inform Proc Syst, 29"},{"key":"4596_CR7","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"4596_CR8","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28"},{"key":"4596_CR9","unstructured":"Mask RCNN, He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer pp 2961\u20132969"},{"key":"4596_CR10","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r 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":"4596_CR11","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (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":"4596_CR12","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"issue":"9","key":"4596_CR13","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.3390\/s21093263","volume":"21","author":"J Yu","year":"2021","unstructured":"Yu J, Zhang W (2021) Face mask wearing detection algorithm based on improved YOLO-v4. Sensors 21(9):3263","journal-title":"Sensors"},{"key":"4596_CR14","doi-asserted-by":"crossref","unstructured":"Yang G, Feng W, Jin J, Lei Q, Li X, Gui G, & Wang W (2020) Face mask recognition system with YOLOV5 based on image recognition. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC) pp 1398\u20131404 IEEE","DOI":"10.1109\/ICCC51575.2020.9345042"},{"key":"4596_CR15","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In European Conference on Computer Vision pp 21\u201337. Springer, Cham","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"4596_CR16","doi-asserted-by":"publisher","first-page":"24344","DOI":"10.1109\/ACCESS.2020.2971026","volume":"8","author":"S Zhai","year":"2020","unstructured":"Zhai S, Shang D, Wang S, Dong S (2020) DF-SSD: An improved SSD object detection algorithm based on DenseNet and feature fusion. IEEE access 8:24344\u201324357","journal-title":"IEEE access"},{"key":"4596_CR17","doi-asserted-by":"crossref","unstructured":"Lin TY, 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":"4596_CR18","doi-asserted-by":"crossref","unstructured":"Chen X, Gupta A (2017) Spatial memory for context reasoning in object detection. In ICCV","DOI":"10.1109\/ICCV.2017.440"},{"key":"4596_CR19","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TIP.2019.2930906","volume":"29","author":"Y Liu","year":"2019","unstructured":"Liu Y, Han J, Zhang Q, Shan C (2019) Deep salient object detection with contextual information guidance. IEEE Trans Image Process 29:360\u2013374","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"4596_CR20","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TMM.2017.2729786","volume":"19","author":"S Tang","year":"2017","unstructured":"Tang S, Li Y, Deng L, Zhang Y (2017) Object localization based on proposal fusion. IEEE Trans Multimedia 19(9):2105\u20132116","journal-title":"IEEE Trans Multimedia"},{"key":"4596_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107149","volume":"100","author":"W Ma","year":"2020","unstructured":"Ma W, Wu Y, Cen F, Wang G (2020) Mdfn: Multi-scale deep feature learning network for object detection. Pattern Recogn 100:107149","journal-title":"Pattern Recogn"},{"key":"4596_CR22","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D (2018) Receptive field block net for accurate and fast object detection. In Proceedings of the European Conference on Computer Vision (ECCV) pp 385\u2013400","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"4596_CR23","doi-asserted-by":"crossref","unstructured":"Li J, Wang Y, Wang C, Tai Y, Qian J, Yang J, Huang F (2019) DSFD: dual shot face detector. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 5060\u20135069","DOI":"10.1109\/CVPR.2019.00520"},{"key":"4596_CR24","doi-asserted-by":"crossref","unstructured":"Liu JJ, Hou Q, Cheng MM, Feng J, Jiang J (2019). A simple pooling-based design for real-time salient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 3917\u20133926","DOI":"10.1109\/CVPR.2019.00404"},{"key":"4596_CR25","doi-asserted-by":"crossref","unstructured":"Wang T, Anwer RM, Cholakkal H, Khan FS, Pang Y, Shao L (2019) Learning rich features at high-speed for single-shot object detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision pp 1971\u20131980","DOI":"10.1109\/ICCV.2019.00206"},{"key":"4596_CR26","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/JSTARS.2020.3041783","volume":"14","author":"S Chen","year":"2020","unstructured":"Chen S, Zhan R, Wang W, Zhang J (2020) Learning slimming SAR ship object detector through network pruning and knowledge distillation. IEEE J Selected Topics Appl Earth Observations Remote Sensing 14:1267\u20131282","journal-title":"IEEE J Selected Topics Appl Earth Observations Remote Sensing"},{"key":"4596_CR27","doi-asserted-by":"crossref","unstructured":"Zhou D, Fang J, Song X, Guan C, Yin J, Dai Y, Yang R (2019). Iou loss for 2d\/3d object detection. In 2019 International Conference on 3D Vision (3DV), pp 85\u201394, IEEE","DOI":"10.1109\/3DV.2019.00019"},{"key":"4596_CR28","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 12993\u201313000)","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"4596_CR29","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"4596_CR30","doi-asserted-by":"crossref","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2736\u20132744)","DOI":"10.1109\/ICCV.2017.298"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04596-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-022-04596-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-022-04596-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T15:19:52Z","timestamp":1668698392000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-022-04596-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,3]]},"references-count":30,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["4596"],"URL":"https:\/\/doi.org\/10.1007\/s11227-022-04596-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,3]]},"assertion":[{"value":"9 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2022","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No need ethical approval Science and Technology Project of Tarim University (No. TDZKZD202104).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No need informed consent.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}