{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:13:32Z","timestamp":1778278412488,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,8]]},"DOI":"10.1145\/3654446.3654500","type":"proceedings-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T18:00:57Z","timestamp":1714759257000},"page":"301-306","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["YOLOv5s object detection method based on separable convolution and SENet"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8314-6448","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"Changchun University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1209-0957","authenticated-orcid":false,"given":"Yaodong","family":"Jia","sequence":"additional","affiliation":[{"name":"Changchun University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104243"},{"key":"e_1_3_2_1_2_1","volume-title":"Rich feature hierarchies for accurate object detection and semantic seg mentation [C] \u2225Proceedings of the IEEE conference on computer vision and pattern recognition","author":"GIR SHICK R","year":"2020","unstructured":"GIR SHICK R, DONA HUE J, DARRELL T, Rich feature hierarchies for accurate object detection and semantic seg mentation [C] \u2225Proceedings of the IEEE conference on computer vision and pattern recognition. Columbus: IEEE, 2020:580-587."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"GIRSHICK R. Fast R-CNN [J]. Computer Science 2015.","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_2_1_4_1","volume-title":"Girshick Ross, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]","author":"SHA QING","year":"2017","unstructured":"Ren SHA O QING, He KAI MING, Girshick Ross, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6):1137-1149."},{"key":"e_1_3_2_1_5_1","volume-title":"Computational Intelligence and Neuroscience","author":"J.","year":"2022","unstructured":"Dai, G., Hu, L., & Fan, J. (2022). DA-ActNN-YOLOV5: Hybrid YOLOv5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification. Computational Intelligence and Neuroscience, 2022."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/358669.358692"},{"key":"e_1_3_2_1_7_1","first-page":"24261","article-title":"An all-mlp architecture for vision [J]","volume":"34","author":"MLP","year":"2021","unstructured":"Tolstikhin I O, Houlsby N, Kolesnikov A, MLP- Mixer: An all-mlp architecture for vision [J]. Advances in Neural Information Processing Systems, 2021, 34:24261- 24272.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","volume-title":"Pruning filtersfor efficient DNNs [C]\/\/Proceedings of 2016 international conference on neuralin for mation processing","author":"LI H","year":"2019","unstructured":"LI H, K ADAV A, DURDANOVICI, etal. Pruning filtersfor efficient DNNs [C]\/\/Proceedings of 2016 international conference on neuralin for mation processing. Berlin: Springer, 2019: 1387-1395."},{"key":"e_1_3_2_1_9_1","first-page":"324","article-title":"Filter pruning viageometric median for deep convolutional networks acceleration[C]\/\/Proceedings of IEEE CONFERENCE ON computer vision and pattern recognition","volume":"2019","author":"HEY LIUP","unstructured":"HEY, LIUP, WANG Z, etal. Filter pruning viageometric median for deep convolutional networks acceleration[C]\/\/Proceedings of IEEE CONFERENCE ON computer vision and pattern recognition. Washington D.C: IEEE, 2019: 324-336.","journal-title":"Washington D.C: IEEE"},{"key":"e_1_3_2_1_10_1","volume-title":"Rethinking the smaller normless informative assumption in channel pruning of convolution layers [EB\/OL].[2020-05-11]. https:\/\/arxiv.org\/abs\/","author":"YE J B","year":"1802","unstructured":"YE J B, LU X, LIN Z, etal. Rethinking the smaller normless informative assumption in channel pruning of convolution layers [EB\/OL].[2020-05-11]. https:\/\/arxiv.org\/abs\/ 1802.00124, 2020."},{"key":"e_1_3_2_1_11_1","volume-title":"Network trimming: a data driven neuron pruning approach wards efficient deep architectures [EB\/OL]. [2020-05-11]. https:\/\/arxiv.org\/ 1607.03250.pdf","author":"HU H Y","year":"2020","unstructured":"HU H Y, PENG R, TAI Y W, Network trimming: a data driven neuron pruning approach wards efficient deep architectures [EB\/OL]. [2020-05-11]. https:\/\/arxiv.org\/ 1607.03250.pdf, 2020."},{"key":"e_1_3_2_1_12_1","volume-title":"You only look once: Unified, real-time object detection [C]","author":"REDMON J","year":"2016","unstructured":"REDMON J, DIVVALA S, GIRSHICK R, You only look once: Unified, real-time object detection [C], 2016: 779-788."},{"key":"e_1_3_2_1_13_1","volume-title":"Defect signal intelligent recognition of weld radiographs based on YOLOV5-IMPROVEMENT","year":"2020","unstructured":"Lushuai Xu, Shaohua Dong, Haotian Wei, Qingying Ren, Jiawei Huang, Jiayue Liu. Defect signal intelligent recognition of weld radiographs based on YOLOV5-IMPROVEMENT. 2020."},{"key":"e_1_3_2_1_14_1","volume-title":"YOLOv3: An IncrementalI mprovement [J]. arXiv eprints","author":"A.","year":"2018","unstructured":"Redmon J, Farhadi A. YOLOv3: An IncrementalI mprovement [J]. arXiv eprints, 2018."},{"key":"e_1_3_2_1_15_1","volume-title":"YOLOv4: optimal speed and accuracy ofobject detection [J]. arXiv","author":"CY H.","year":"2004","unstructured":"Bochkovskiy A, Wang CY, Liao H. YOLOv4: optimal speed and accuracy ofobject detection [J]. arXiv: 2004.10934."},{"key":"e_1_3_2_1_16_1","volume-title":"Feature pyramid networks f-or object detection [C]\/\/ 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"LIN T Y","year":"2019","unstructured":"LIN T Y, DOLL\u00c1R P, GIRSHICK R, Feature pyramid networks f-or object detection [C]\/\/ 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2019: 936-944."},{"key":"e_1_3_2_1_17_1","volume-title":"Path aggregation network for instance seg-mentation [C]\/\/ 2018 IEEE\/CVF Conference on Computer Vision and Pattern Re-cognition. Salt Lake City","author":"LIU S","year":"2018","unstructured":"LIU S, QI L, QIN H F, Path aggregation network for instance seg-mentation [C]\/\/ 2018 IEEE\/CVF Conference on Computer Vision and Pattern Re-cognition. Salt Lake City, USA: IEEE, 2018: 8759-8768."},{"key":"e_1_3_2_1_18_1","volume-title":"Proceeding-s of the IEEE Conference on Computer Vision and Pat tern Recognition, NewYork: IEEE","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks [C]\/\/ Proceeding-s of the IEEE Conference on Computer Vision and Pat tern Recognition, NewYork: IEEE, 2018: 7132-7141."}],"event":{"name":"SPCNC 2023: The 2nd International Conference on Signal Processing, Computer Networks and Communications","location":"Xiamen China","acronym":"SPCNC 2023"},"container-title":["Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654446.3654500","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3654446.3654500","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:35:43Z","timestamp":1755891343000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3654446.3654500"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":18,"alternative-id":["10.1145\/3654446.3654500","10.1145\/3654446"],"URL":"https:\/\/doi.org\/10.1145\/3654446.3654500","relation":{},"subject":[],"published":{"date-parts":[[2023,12,8]]},"assertion":[{"value":"2024-05-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}