MiniYOLO: A lightweight object detection algorithm that realizes the trade‐off between model size and detection accuracy

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 12135-12151 RESEARCH ARTICLE MiniYOLO: A lightweight object detection algorithm that realizes the trade-off between model size and detection accuracy Yi Liu, Yi Liu Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this authorChangsheng Zhang, Corresponding Author Changsheng Zhang [email protected] orcid.org/0000-0002-8058-9809 Department of Software Engineering, Northeastern University, Shenyang, China Correspondence Changsheng Zhang, Department of Software Engineering, Northeastern University, Shenyang, China. Email: [email protected]Search for more papers by this authorWenjing Wu, Wenjing Wu Department of Computer Application Technology, Northeastern University, Shenyang, ChinaSearch for more papers by this authorBin Zhang, Bin Zhang Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this authorFucai Zhou, Fucai Zhou Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this author Yi Liu, Yi Liu Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this authorChangsheng Zhang, Corresponding Author Changsheng Zhang [email protected] orcid.org/0000-0002-8058-9809 Department of Software Engineering, Northeastern University, Shenyang, China Correspondence Changsheng Zhang, Department of Software Engineering, Northeastern University, Shenyang, China. Email: [email protected]Search for more papers by this authorWenjing Wu, Wenjing Wu Department of Computer Application Technology, Northeastern University, Shenyang, ChinaSearch for more papers by this authorBin Zhang, Bin Zhang Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this authorFucai Zhou, Fucai Zhou Department of Software Engineering, Northeastern University, Shenyang, ChinaSearch for more papers by this author First published: 20 September 2022 https://doi.org/10.1002/int.23079Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The object detection task is to locate and classify objects in an image. The current state-of-the-art high-accuracy object detection algorithms rely on complex networks and high computational cost. These algorithms have high requirements on the memory resource and computing capability of the deployed device, and are difficult to apply to mobile and embedded devices. Through the depthwise separable convolution and multiple efficient network structures, this paper designs a lightweight backbone network and two different multiscale feature fusion structures, and proposes a lightweight one-stage object detection algorithm—MiniYOLO. With the model size of only 4.2 MB, MiniYOLO still maintains a high detection accuracy, realizing the trade-off between the model size and detection accuracy. Experimental results on MS COCO 2017 data set show that compared to the state-of-the-art PP-YOLO-tiny, MiniYOLO achieves higher mAP with the same model size. Compared with other lightweight object detection algorithms, MiniYOLO has certain advantages in detection accuracy or model size. The code associated with this paper can be downloaded from https://github.com/CaedmonLY/MiniYOLO/. Volume37, Issue12December 2022Pages 12135-12151 RelatedInformation