{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:05:33Z","timestamp":1757455533578,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11063-022-10952-0","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T06:58:37Z","timestamp":1658991517000},"page":"1583-1603","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluation of Physical Electrical Experiment Operation Process Based on YOLOv5 and ResNeXt Cascade Networks"],"prefix":"10.1007","volume":"55","author":[{"given":"Wenbin","family":"Zeng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3130-1685","authenticated-orcid":false,"given":"Jichang","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Luguo","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Jianfei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"issue":"03","key":"10952_CR1","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1142\/S1793351X16500045","volume":"10","author":"X Hao","year":"2016","unstructured":"Hao X, Zhang G, Ma S (2016) Deep Learning. International Journal of Semantic Computing. 10(03):417\u2013439","journal-title":"International Journal of Semantic Computing."},{"key":"10952_CR2","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 1097-1105"},{"key":"10952_CR3","first-page":"2961","volume":"2017","author":"K He","year":"2017","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2017:2961\u20132969","journal-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)"},{"key":"10952_CR4","doi-asserted-by":"crossref","unstructured":"Bertinetto L et al (2016) Fully-Convolutional Siamese Networks for Object Tracking. European Conference on Computer Vision Springer, Cham","DOI":"10.1007\/978-3-319-48881-3_56"},{"issue":"6","key":"10952_CR5","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 R, Sun J (2017) Faster R-CNN: Towards RealTime Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10952_CR6","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. (2016) SSD: Single Shot MultiBox Detector. Computer Vision - ECCV 2016, vol 9905","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10952_CR7","first-page":"1125","volume":"15","author":"J Redmon","year":"2018","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. IEEE Trans. Pattern Anal. 15:1125\u20131131","journal-title":"IEEE Trans. Pattern Anal."},{"key":"10952_CR8","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv e-prints, arXiv:2004.10934"},{"key":"10952_CR9","unstructured":"Jocher G, Nishimura K, Mineeva T, Vilari\u00f1o R (2021) YOLOv5. latest version available at https:\/\/github.com\/ultralytics\/yolov5. last accessed March 1"},{"key":"10952_CR10","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2021) Scaled-YOLOv4: Scaling Cross Stage Partial Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 029-13038","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"10952_CR11","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, et al. (2014) Microsoft coco: Common objects in context. In: European conference on computer vision. 740-755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10952_CR12","unstructured":"Purkait P, Zhao C, Zach C (2018) SPP-Net: Deep Absolute Pose Regression with Synthetic Views[J]. CVPR"},{"key":"10952_CR13","doi-asserted-by":"crossref","unstructured":"Wang K, Liew JH, Zou Y, et al. (2019) Panet: Few-shot image semantic segmentation with prototype alignment. Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9197-9206","DOI":"10.1109\/ICCV.2019.00929"},{"key":"10952_CR14","doi-asserted-by":"crossref","unstructured":"Lin TY , Dollar P, Girshick R , et al. (2017) Feature Pyramid Networks for Object Detection[J]. IEEE Computer Society","DOI":"10.1109\/CVPR.2017.106"},{"key":"10952_CR15","doi-asserted-by":"crossref","unstructured":"Krishna K, Narasimha Murty M (1999) Genetic K-Means Algorithm. IEEE Transactions on systems, Part B: cybernetics, vol. 29, no. 3","DOI":"10.1109\/3477.764879"},{"key":"10952_CR16","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak YK, et al (2019) Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE","DOI":"10.1109\/CVPR.2019.00075"},{"key":"10952_CR17","doi-asserted-by":"crossref","unstructured":"Nowozin S (2014) Optimal decisions from probabilistic models: The intersection-overunion case. In: CVPR","DOI":"10.1109\/CVPR.2014.77"},{"issue":"07","key":"10952_CR18","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng Z, Wang P, Liu W et al (2020) Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence 34(07):12993\u201313000","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10952_CR19","volume-title":"Andrew Zisserman","author":"K Simonyan","year":"2015","unstructured":"Simonyan K (2015) Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition, Computer Science"},{"key":"10952_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, et al (2015) Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-9","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10952_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770-778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10952_CR22","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, et al. (2017) Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4700-4708","DOI":"10.1109\/CVPR.2017.243"},{"key":"10952_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al (2016) Rethinking the Inception Architecture for Computer Vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2818-2826","DOI":"10.1109\/CVPR.2016.308"},{"key":"10952_CR24","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Dollar P, et al. (2017) Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1492-1500","DOI":"10.1109\/CVPR.2017.634"},{"key":"10952_CR25","unstructured":"Iandola FN, Han S, et al (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and less than 0.5MB model size. ICLR 2017 conference on Computer Vision and Pattern Recognition"},{"key":"10952_CR26","unstructured":"Paszke A, Gross S, Massa F, et al. (2019) PyTorch: An Imperative Style, High-Performance Deep Learning Library. Part of Advances in Neural Information Processing Systems. 32, (NeurIPS)"},{"key":"10952_CR27","doi-asserted-by":"crossref","unstructured":"Kisantal M, Wojna Z, Murawski J, et al. (2019) Augmentation for small object detection. 9th International Conference on Advances in Computing and Information Technology","DOI":"10.5121\/csit.2019.91713"},{"key":"10952_CR28","unstructured":"Kingma DP, Ba J (2015) Adam : A method for Stochastic Optimization, that the name is derived from adaptive moment estimation. The 3rd International Conference for Learning Representations, San Diego"},{"key":"10952_CR29","doi-asserted-by":"crossref","unstructured":"Antol S, Agrawal A, et al. (2015) VQA: Visual Question Answering. Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2425-2433","DOI":"10.1109\/ICCV.2015.279"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10952-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10952-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10952-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T02:32:39Z","timestamp":1682562759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10952-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["10952"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10952-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,7,26]]},"assertion":[{"value":"29 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 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 author declares that this research does not violate any ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"Informed consent was obtained from all individual participants involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent Statement"}}]}}