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J. Neur. Syst."],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:p> Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector\u2019s complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because the single-level feature map cannot balance deep semantic information and shallow detail information. We introduce a novel detector\u00a0\u2014 the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector to address these challenges. The SNPMiSo detector is constructed based on SNP-like neurons. In SNPMiSo, we employ two kinds of Transformers to boost the important features across different-level feature maps separately. After enhancing the features, we use an incremental upsampling module to upsample and merge the two feature maps. This combined feature map is input into the NAF dilated residual module and the NAF dual-branch detection head. This process allows us to extract multi-scale features and carry out detection tasks. Our tests show promising results: On the COCO dataset, SNPMiSo attains an Average Precision (AP) of 38.7, an improvement of 1.0 AP over YOLOF.\u00a0In addition, SNPMiSo demonstrates a quicker detection speed, outperforming some advanced multi-level and single-level object detectors. <\/jats:p>","DOI":"10.1142\/s0129065724500357","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T09:42:56Z","timestamp":1711100576000},"source":"Crossref","is-referenced-by-count":14,"title":["Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4345-1241","authenticated-orcid":false,"given":"Zhengyuan","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8917-5060","authenticated-orcid":false,"given":"Siyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4736-0164","authenticated-orcid":false,"given":"Hong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5283-9954","authenticated-orcid":false,"given":"Zhicai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3422-104X","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"S0129065724500357BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"S0129065724500357BIB002","doi-asserted-by":"publisher","DOI":"10.3233\/ICA-230715"},{"key":"S0129065724500357BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"S0129065724500357BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01284"},{"issue":"2","key":"S0129065724500357BIB005","first-page":"279","volume":"71","author":"Ionescu M.","year":"2006","journal-title":"Fundam. 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