Dual-Branch Diffusion Detection Model for Photovoltaic Array and Hotspot Defect Detection in Infrared Images

Abstract

Failures in solar photovoltaic (PV) modules generate heat, leading to various hotspots observable in infrared images. Automated hotspot detection technology enables rapid fault identification in PV systems, while PV array detection, leveraging geometric cues from infrared images, facilitates the precise localization of defects. This study tackles the complexities of detecting PV array regions and diverse hotspot defects in infrared imaging, particularly under the conditions of complex backgrounds, varied rotation angles, and the small scale of defects. The proposed model encodes infrared images to extract semantic features, which are then processed through an PV array detection branch and a hotspot detection branch. The array branch employs a diffusion-based anchor-free mechanism with rotated bounding box regression, enabling the robust detection of arrays with diverse rotational angles and irregular layouts. The defect branch incorporates a novel inside-awareness loss function designed to enhance the detection of small-scale objects. By explicitly modeling the dependency distribution between arrays and defects, this loss function effectively reduces false positives in hotspot detection. Experimental validation on a comprehensive PV dataset demonstrates the superiority of the proposed method, achieving a mean average precision (mAP) of 71.64% for hotspot detection and 97.73% for PV array detection.

Keywords

• hotspot defect detection
• photovoltaic array detection
• diffusion model
• dual-branch framework
• inside-awareness loss
• deep learning