A Data-Augmented Attention-Driven Framework for Infrared Object Detection in IoT-Connected Substations

A Data-Augmented Attention-Driven Framework for Infrared Object Detection in IoT-Connected Substations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 August 2025 V1 Latest version Share on A Data-Augmented Attention-Driven Framework for Infrared Object Detection in IoT-Connected Substations Authors : Mengbo Ju , Xiaoning Jiang [email protected] , Shouguang Wang , Bingxiao Yu , and Zhigang Gan Authors Info & Affiliations https://doi.org/10.22541/au.175578498.89152638/v1 148 views 111 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the context of IoT-enabled smart grid systems, reliable and accurate monitoring of substation equipment is crucial for ensuring operational safety. This paper proposes an infrared object detection framework tailored for substation environments, integrating enhanced data augmentation and an improved attention mechanism. The dataset was expanded by introducing thermal disturbance and edge enhancement, which increased precision from 0.798 to 0.910, recall from 0.695 to 0.777, and mAP@50 from 0.782 to 0.859. On the YOLOv10 backbone, we designed a lightweight CGCFA module, incorporating ODConv and BiFPN structures, reducing model complexity from 8.2 GFLOPs to 6.7 GFLOPs while further improving precision to 0.946, recall to 0.806, and mAP@50 to 0.873. Experimental results demonstrate that the proposed method achieves superior detection accuracy and efficiency, making it well-suited for real-time monitoring in IoT-connected substations. Supplementary Material File (a_data_augmented_attention_driven_framework_for_infrared_object_detection_in_iot_connected_substations.pdf) Download 4.08 MB Information & Authors Information Version history V1 Version 1 21 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords edge computing infrared image iot-enabled substation monitoring yolov10 Authors Affiliations Mengbo Ju Zhejiang Gongshang University View all articles by this author Xiaoning Jiang [email protected] Zhejiang Gongshang University View all articles by this author Shouguang Wang Zhejiang Gongshang University View all articles by this author Bingxiao Yu Zhejiang Gongshang University View all articles by this author Zhigang Gan Zhejiang Gongshang University View all articles by this author Metrics & Citations Metrics Article Usage 148 views 111 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mengbo Ju, Xiaoning Jiang, Shouguang Wang, et al. A Data-Augmented Attention-Driven Framework for Infrared Object Detection in IoT-Connected Substations. Authorea . 21 August 2025. DOI: https://doi.org/10.22541/au.175578498.89152638/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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