{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:21:50Z","timestamp":1763544110717,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Henan","award":["252300421063","24ZX005","62076223","DL-2023Z-198"],"award-info":[{"award-number":["252300421063","24ZX005","62076223","DL-2023Z-198"]}]},{"name":"Research Project of Henan Province Universities","award":["252300421063","24ZX005","62076223","DL-2023Z-198"],"award-info":[{"award-number":["252300421063","24ZX005","62076223","DL-2023Z-198"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["252300421063","24ZX005","62076223","DL-2023Z-198"],"award-info":[{"award-number":["252300421063","24ZX005","62076223","DL-2023Z-198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Third Batch of Science and Technology Projects for Production Frontline of State Grid Jiangsu Electric Power Co., Ltd.","award":["252300421063","24ZX005","62076223","DL-2023Z-198"],"award-info":[{"award-number":["252300421063","24ZX005","62076223","DL-2023Z-198"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The You Only Look Once (YOLO) object detection model has been widely applied to electric power operation violation identification, owing to its balanced performance in detection accuracy and inference speed. However, it still faces the following challenges: (1) insufficient detection capability for irregularly shaped objects; (2) objects with low object-background contrast are easily omitted; (3) improving detection accuracy while maintaining computational efficiency is difficult. To address the above challenges, a novel real-time object detection model is proposed in this paper, which introduces three key innovations. To handle the first challenge, an edge perception cross-stage partial fusion with two convolutions (EPC2f) module that combines edge convolutions with depthwise separable convolutions is proposed, which can enhance the feature representation of irregularly shaped objects with only a slight increase in parameters. To handle the second challenge, an adaptive combination of local and global features module is proposed to enhance the discriminative ability of features while maintaining computational efficiency, where the local and global features are extracted respectively via 1D convolutions and adaptively combined by using learnable weights. To handle the third challenge, a parameter sharing of a multi-scale detection heads scheme is proposed to reduce the number of parameters and improve the interaction between multi-scale detection heads. The ablation study on the Ali Tianchi competition dataset validates the effectiveness of three innovation points and their combination. EAP-YOLO achieves the mAP@0.5 of 93.4% and an mAP@0.5\u20130.95 of 70.3% on the Ali Tianchi Competition dataset, outperforming 12 other object detection models while satisfying the real-time requirement.<\/jats:p>","DOI":"10.3390\/info16070569","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:07:17Z","timestamp":1751515637000},"page":"569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Real-Time Object Detection Model for Electric Power Operation Violation Identification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4328-6411","authenticated-orcid":false,"given":"Xiaoliang","family":"Qian","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxiang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8770-3862","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.56028\/aetr.10.1.249.2024","article-title":"A Digital Advocacy and Leadership Mechanism that Empowers the Construction of Digital\u2014Intelligent Strong Power Grid","volume":"10","author":"Wang","year":"2024","journal-title":"Adv. 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