## 1 Introduction
The model, augmented with DySample (dynamic sampling), EMA (multi-scale attention) and ACMix (convolution-self-attention fusion) modules, exhibited superior performance in comparison to existing methods, achieving 94.43% [EMAIL_ADDRESS] and 84.50% [EMAIL_ADDRESS] in 2215+960 panel images. Its efficacy lies in its capacity for fine defect detection, with the concomitant potential to mitigate instances of false positives [14]. [PERSON] et al. YOLOv9 prompting outperformed user points prompting in segmentation tasks, highlighting its robustness in various lighting conditions and resolutions [15]. A study by [PERSON] et al. [16] investigated the application of a Convolutional Neural Network (CNN), known as YOLOv8, with Particle Swarm Optimization (PSO) to detect faults in photovoltaic (PV) cells to improve batch size, anchor box size, learning rate, and detection accuracy [17; 18]. Two case studies were conducted using 70% and 80% training sets, respectively. The dataset employed in the current study contained 2624 images of solar panel cells, meticulously labeled and adjusted to ensure consistency. By capitalizing on the strengths of YOLOv8 and PSO, a mean average precision (mAP) of 94% was attained. In their study, [PERSON] et al. [19] comprehensively evaluated and compared the effectiveness of various YOLO models, ranging from YOLOv5 to YOLOv9, in detecting faults in thermal images of solar panels. For both the training and evaluation processes, the dataset employed was sourced from the M. Lee Roboflow Solar Panel Dataset [20], which is available on Roboflow. This initial set comprised 2232 images, which were then categorized into three distinct classes: bypass diodes, cell faults, and hotspots. The dataset was then divided into training and validation sets at an 85:15 ratio, yielding 1897 images for the training phase and 335 for the validation set. Subsequently, the authors trained the YOLO models, including their GELAN variant, for 300 epochs with batch sizes of 8 and 16. Upon completing this process, the test results were analyzed, and it was ascertained that YOLOv8, YOLOv9, and GELAN exhibited a considerable degree of efficacy in detecting faults in solar panel images. Notably, the GELANc model achieved a mAP of 70.4%, surpassing the 64.5% mAP of the YOLOv9c model. This finding indicates that the GELANc model exhibits superior accuracy and efficiency compared to the other models[PERSON]  
In a recent study, [PERSON] et al. [21] sought to enhance the YOLOv9 algorithm by integrating image processing techniques to achieve effective hotspot detection in solar PV panels. This study's implementation involved the utilization of a dataset comprising 1558 thermal images derived from a solar PV farm situated in Pasir Mas, Kelantan, Malaysia. A drone with an infrared (IR) camera was flown at heights of 15-30 m above ground to obtain thermal images. After completing 100 iterations of a training process, the model demonstrated recall values exceeding 97% and precision and mAP (mean average precision) metrics surpassing 96%. These outcomes underscore the model's remarkable performance, substantiating its efficacy and dependability in object detection tasks. A study was conducted by [PERSON] et al. [22] on solar panel detection in satellite images. The investigation commenced with the Faster Mask-CNN architecture [23] before transitioning to YOLO models. In order to do so, this study tested several iterations of YOLOv9 [24], including YOLOv9c and YOLOv9e. This study also tested the recent YOLOv10 [25] variants YOLOv10x and YOLOv10l. The dataset comprised 3480 images, 2745 of which featured ground and 735 rooftop backgrounds. The images were sourced from solar PV samples captured through satellite and aerial photography in Beijing, China [26], with the YOLO models using 3360 images for training, 90 for validation, and 30 for testing. The Mask-CNN baseline model utilized 3415 images for training and 65 for testing.