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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p> The measurement of excavator pose information is crucial for advancing intelligent excavator control systems. To address challenges in excavator pose detection \u2014 such as small pose measurement targets and blurred images \u2014 a high-speed, high-accuracy visual technology-based pose recognition algorithm, super-resolution input-YOLOv5s, was developed. This algorithm includes an image target designed specifically to facilitate the detection of the excavator arm\u2019s pose angles. Excavator pose information is derived through the analysis of the target image data. The SRGAN model is used to enhance the quality of input data for YOLOv5, while attention mechanisms are introduced at the terminal stage of the backbone network. Focal loss is employed as the loss function to improve the detection accuracy and stability for small targets in complex construction environments, while also mitigating class imbalance. Experimental results demonstrate that the improved algorithm, SRI-YOLOv5s, achieved a detection speed of 59.20 FPS, with a mean average precision (mAP) of 89.46%, precision of 91.7%, and recall of 92.1%, outperforming the original model. The model\u2019s real-time performance and robustness meet the requirements for excavator pose detection in practical environments. <\/jats:p>","DOI":"10.1142\/s021800142554014x","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T06:02:59Z","timestamp":1752732179000},"source":"Crossref","is-referenced-by-count":0,"title":["SRGAN-Based Input Enhancement and Attention-Guided YOLOv5s for Real-Time Excavator Pose Estimation"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7617-1508","authenticated-orcid":false,"given":"Wangting","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian District, Tangshan 063210, Hebei, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9996-5878","authenticated-orcid":false,"given":"Qixiang","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian District, Tangshan 063210, Hebei, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7838-7958","authenticated-orcid":false,"given":"Ke","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian District, Tangshan 063210, Hebei, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-9827","authenticated-orcid":false,"given":"Xuedong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian District, Tangshan 063210, Hebei, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8933-9168","authenticated-orcid":false,"given":"Bo","family":"Cui","sequence":"additional","affiliation":[{"name":"Hebei Key Laboratory of Industrial Intelligent Perception, College of Artificial Intelligence, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian District, Tangshan 063210, Hebei, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"S021800142554014XBIB001","first-page":"156","volume":"771","author":"Baqqal I.","year":"2023","journal-title":"Artif. 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