An Improved YOLOv8n Method for Small Thermal Defect Detection of Photovoltaic Modules in UAV Infrared Inspection.
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| Title: | An Improved YOLOv8n Method for Small Thermal Defect Detection of Photovoltaic Modules in UAV Infrared Inspection. |
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| Authors: | He, Tengfei1 (AUTHOR), Mao, Zhongyuan1 (AUTHOR), Zhong, Yuanchang1 (AUTHOR) zyc@cqu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1986. 21p. |
| Subjects: | Photovoltaic power generation, Object recognition (Computer vision), Deep learning, Machine learning |
| Abstract: | Highlights: What are the main findings? A lightweight task-specific YOLOv8n-based detector is proposed for small thermal defect detection of photovoltaic modules in UAV infrared inspection. The proposed method improves detection accuracy and localization quality while maintaining only 1.03 M parameters, a 2.4 MB model size, and real-time inference performance. What are the implications of the main findings? The study demonstrates that task-oriented network redesign is effective for detecting small, weak, and boundary-ambiguous thermal defects in UAV infrared photovoltaic inspection. The proposed method offers a lightweight and practical solution for real-time photovoltaic module inspection under complex infrared backgrounds. To address missed detections, false alarms, and deployment limitations in thermal defect detection of photovoltaic modules from unmanned aerial vehicle (UAV) infrared images, this paper proposes an improved detection method based on You Only Look Once version 8 nano (YOLOv8n). The proposed method is optimized according to the characteristics of UAV infrared photovoltaic inspection, including small thermal targets, weak and diffuse thermal responses, complex backgrounds, and lightweight deployment requirements. Specifically, a P2 shallow feature layer is introduced to enhance fine-grained feature perception for small thermal defects, while Ghost Convolution (GhostConv) is incorporated into the backbone to reduce model complexity. In addition, C2f-Large Separable Kernel Attention (C2f-LSKA) is embedded in the neck to strengthen contextual and spatial feature modeling under complex infrared backgrounds, and Wise-IoU version 3 (WIoUv3) is adopted to improve bounding box regression and localization stability for boundary-ambiguous thermal anomalies. Experiments are conducted on a self-constructed UAV infrared thermal imaging dataset. From nearly 10,000 inspection images, 3000 representative images are selected and manually annotated, covering typical challenges such as small hot spots, low-contrast defects, complex background interference, and diffuse abnormal temperature-rise regions. Compared with the baseline YOLOv8n, the proposed method improves Precision, Recall, mean average precision at an IoU threshold of 0.5 (mAP@0.5), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) by 5.1, 11.4, 9.6, and 13.2 percentage points, respectively, while reducing the number of parameters and model size by 65.8% and 61.9%, respectively. These results indicate that the proposed method improves detection accuracy and localization quality under the evaluated UAV infrared inspection setting while maintaining lightweight characteristics. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A lightweight task-specific YOLOv8n-based detector is proposed for small thermal defect detection of photovoltaic modules in UAV infrared inspection. The proposed method improves detection accuracy and localization quality while maintaining only 1.03 M parameters, a 2.4 MB model size, and real-time inference performance. What are the implications of the main findings? The study demonstrates that task-oriented network redesign is effective for detecting small, weak, and boundary-ambiguous thermal defects in UAV infrared photovoltaic inspection. The proposed method offers a lightweight and practical solution for real-time photovoltaic module inspection under complex infrared backgrounds. To address missed detections, false alarms, and deployment limitations in thermal defect detection of photovoltaic modules from unmanned aerial vehicle (UAV) infrared images, this paper proposes an improved detection method based on You Only Look Once version 8 nano (YOLOv8n). The proposed method is optimized according to the characteristics of UAV infrared photovoltaic inspection, including small thermal targets, weak and diffuse thermal responses, complex backgrounds, and lightweight deployment requirements. Specifically, a P2 shallow feature layer is introduced to enhance fine-grained feature perception for small thermal defects, while Ghost Convolution (GhostConv) is incorporated into the backbone to reduce model complexity. In addition, C2f-Large Separable Kernel Attention (C2f-LSKA) is embedded in the neck to strengthen contextual and spatial feature modeling under complex infrared backgrounds, and Wise-IoU version 3 (WIoUv3) is adopted to improve bounding box regression and localization stability for boundary-ambiguous thermal anomalies. Experiments are conducted on a self-constructed UAV infrared thermal imaging dataset. From nearly 10,000 inspection images, 3000 representative images are selected and manually annotated, covering typical challenges such as small hot spots, low-contrast defects, complex background interference, and diffuse abnormal temperature-rise regions. Compared with the baseline YOLOv8n, the proposed method improves Precision, Recall, mean average precision at an IoU threshold of 0.5 (mAP@0.5), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) by 5.1, 11.4, 9.6, and 13.2 percentage points, respectively, while reducing the number of parameters and model size by 65.8% and 61.9%, respectively. These results indicate that the proposed method improves detection accuracy and localization quality under the evaluated UAV infrared inspection setting while maintaining lightweight characteristics. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121986 |
