{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:58:48Z","timestamp":1785405528187,"version":"3.56.0"},"reference-count":28,"publisher":"Optica Publishing Group","issue":"11","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U2013602"],"award-info":[{"award-number":["U2013602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022YFB4601802"],"award-info":[{"award-number":["2022YFB4601802"]}]},{"name":"Major Internal Cultivation Project of Harbin Institute of Technology","award":["2023FRFK01001"],"award-info":[{"award-number":["2023FRFK01001"]}]},{"name":"National Independent Project of China","award":["SKLR202301A12"],"award-info":[{"award-number":["SKLR202301A12"]}]},{"name":"Key Technology Research and Development of Suzhou","award":["SYG2024083"],"award-info":[{"award-number":["SYG2024083"]}]},{"name":"Key Technology Research and Development of Suzhou","award":["SYG2024142"],"award-info":[{"award-number":["SYG2024142"]}]},{"name":"School Enterprise Joint Research and Development Center for Cemented Carbide Cutting Tools of Shenzhen Polytechnic University","award":["602431003PQ"],"award-info":[{"award-number":["602431003PQ"]}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Appl. Opt."],"published-print":{"date-parts":[[2026,4,10]]},"abstract":"<jats:p>Achieving robust robotic perception in highly dynamic environments requires accurate and low-latency visual object detection. However, conventional RGB cameras often exhibit limited performance in such fast-changing scenarios due to significant blind time, imaging latency, and motion blur. In this context, event cameras have emerged as a promising solution, offering microsecond-level temporal resolution, high dynamic range, and immunity to motion blur. However, despite these significant theoretical advantages, algorithms for event cameras are still at a nascent stage. Existing approaches, including both transformers and traditional CNNs, struggle to achieve high accuracy and low latency simultaneously, thereby limiting their effectiveness for object detection in highly dynamic environments. Notably, recent studies such as ConvNeXt suggest that modernized CNNs can outperform both vision transformers and traditional CNNs by adopting transformer-like architectural designs. Motivated by this insight, we propose a lightweight CNN-based object detection model, EvConvLite. By strategically \u201cmodernizing\u201d the traditional CNNs, our model not only achieves 3\u00d7 inference speedup, but also surpasses state-of-the-art transformer-based models in detection accuracy. To the best of our knowledge, this is the first reported instance of a lightweight CNN-based model outperforming transformers in event camera object detection accuracy.<\/jats:p>","DOI":"10.1364\/ao.590967","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T15:00:12Z","timestamp":1774278012000},"page":"3748","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient event-based object detection with lightweight CNNs: a surprising advantage over\u00a0transformers"],"prefix":"10.1364","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3564-1624","authenticated-orcid":true,"given":"Sen","family":"Zhang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaofeng","family":"Cheng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangji","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2727-5613","authenticated-orcid":true,"given":"Xiuwen","family":"Bi","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9695-1940","authenticated-orcid":true,"given":"Fusheng","family":"Zha","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mantian","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Polytechnic University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lining","family":"Sun","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"ao-65-11-3748-R1","first-page":"13884","article-title":"Recurrent vision transformers for object detection with event cameras","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Gehrig","year":"2023"},{"key":"ao-65-11-3748-R2","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","type":"journal-article","volume":"44","author":"Gallego","year":"2020","journal-title":"IEEE Trans. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}