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However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet\u2019s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.<\/jats:p>","DOI":"10.1155\/2022\/4437446","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T14:35:19Z","timestamp":1650897319000},"page":"1-10","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Neural Network Based on Circular Representation for Target Detection"],"prefix":"10.1155","volume":"2022","author":[{"given":"Cong","family":"Lin","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang \/524025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhoujian","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang \/524025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiquan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang \/524025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang \/524025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-0057","authenticated-orcid":true,"given":"Wencai","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Data Engineering and Sciences, University of Saint Joseph, Macao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3480-7641","authenticated-orcid":true,"given":"Qiong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing \/100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.003"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/2.294849"},{"key":"3","volume-title":"Fast circle detection using gradient pair vectors","author":"A. 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