A Study on an Internal Temperature Anomaly Detection Strategy for Inverters Based on an Enhanced YOLOv8 Approach

A Study on an Internal Temperature Anomaly Detection Strategy for Inverters Based on an Enhanced YOLOv8 Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on an Internal Temperature Anomaly Detection Strategy for Inverters Based on an Enhanced YOLOv8 Approach Shizhou Xu, Shuo Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9373711/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Detecting abnormal internal temperatures in inverters is a critical aspect of ensuring the safe operation of power equipment. To address the shortcomings of existing methods in detecting minute thermal targets, multi-scale feature fusion, and quantitative temperature estimation, we propose the Temp YOLO algorithm based on an enhanced YOLOv8. This algorithm innovatively integrates a hybrid backbone network combining EfficientViT and MobileNetV3, balancing global feature extraction with the need for a lightweight architecture, thereby effectively enhancing its representational capabilities; A temperature-aware bidirectional feature pyramid network (T-BiFPN) is designed, which significantly improves the localisation accuracy of minute thermal anomaly regions through cross-scale feature fusion and temperature-priority guidance; a multi-task dynamic detection head is developed to simultaneously achieve target classification, bounding box regression, and temperature level estimation, thereby overcoming the limitations of traditional single-task detection. In validation using a self-built inverter infrared image dataset, Temp YOLO achieved an [email protected] of 9.28%, representing a 535.6% improvement over the baseline model YOLOv8n, whilst reducing computational load (GFLOPs) by 60.1% and model parameters to 2.7 million, with a frame rate of 65 frames per second, thereby meeting real-time monitoring requirements. Experiments demonstrate that this method outperforms state-of-the-art (SOTA) models such as YOLOv8s and PP-YOLO in terms of detection accuracy and robustness for small targets (e.g., overheating of IGBT pins) in complex scenarios. Ablation studies confirm significant synergistic effects among the modules, with the T-BiFPN contributing a 12.4% improvement in mAP for small targets. This method achieves significant improvements in detection accuracy and computational efficiency compared to baseline models, providing a viable technical solution for real-time thermal management of inverters and demonstrating strong application potential in specific scenarios. Inverter Temperature anomaly detection YOLOv8 Multi-task output attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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To address the shortcomings of existing methods in detecting minute thermal targets, multi-scale feature fusion, and quantitative temperature estimation, we propose the Temp YOLO algorithm based on an enhanced YOLOv8. This algorithm innovatively integrates a hybrid backbone network combining EfficientViT and MobileNetV3, balancing global feature extraction with the need for a lightweight architecture, thereby effectively enhancing its representational capabilities; A temperature-aware bidirectional feature pyramid network (T-BiFPN) is designed, which significantly improves the localisation accuracy of minute thermal anomaly regions through cross-scale feature fusion and temperature-priority guidance; a multi-task dynamic detection head is developed to simultaneously achieve target classification, bounding box regression, and temperature level estimation, thereby overcoming the limitations of traditional single-task detection. In validation using a self-built inverter infrared image dataset, Temp YOLO achieved an [email protected] of 9.28%, representing a 535.6% improvement over the baseline model YOLOv8n, whilst reducing computational load (GFLOPs) by 60.1% and model parameters to 2.7\u0026nbsp;million, with a frame rate of 65 frames per second, thereby meeting real-time monitoring requirements. Experiments demonstrate that this method outperforms state-of-the-art (SOTA) models such as YOLOv8s and PP-YOLO in terms of detection accuracy and robustness for small targets (e.g., overheating of IGBT pins) in complex scenarios. Ablation studies confirm significant synergistic effects among the modules, with the T-BiFPN contributing a 12.4% improvement in mAP for small targets. 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