ABSTRACT Against the backdrop of the global energy transition, solar photovoltaic (PV) technology has garnered significant attention due to its environmentally friendly attributes and has exhibited rapid growth trends. While the rapid deployment of PV systems facilitates installation, accompanying maintenance challenges cannot be overlooked. To address the issue of multi‐fault detection in PV panel imagery, this study proposes an innovative algorithm based on You Only Look Once (YOLO)v8‐DA. The proposed algorithm first employs the CSPNeXt backbone network to replace YOLOv8's original backbone network, striking a balance between parameter scale and computational cost, thereby creating a faster and more efficient object detection mechanism. Secondly, it introduces the dynamic anchor assignment strategy (Dynamic ATSS) and the dynamic detection head technologies. These innovations resolve the issues of imprecise label assignment and fixed detection heads inherent in traditional methods by incorporating dynamic label assignment and adjusting detection heads during the prediction process. Finally, the study proposes combining 1 × 1 convolutional blocks with CloAttention, enhancing the model's ability to recognise complex defects while maintaining efficiency and practicality. Experimental results demonstrate that the model performs exceptionally well on the dataset, achieving a mean average precision of 95.1%, which is 3.3% higher than the original YOLOv8, effectively validating the model's efficacy.
Lijuan et al. (Thu,) studied this question.