# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 1 Introduction  
With the rapid development of photovoltaic power generation technology [1], the defect detection of photovoltaic panels has become an important link to ensure the efficient operation of photovoltaic systems and extend the service life of equipment [2]. Photovoltaic panels are exposed to complex natural conditions in outdoor environments for a long time, including high temperatures, ultraviolet rays, humidity, sandstorms, etc. These factors can cause different types of defects on the surface and inside of photovoltaic panels, such as cracks, fractures, dust, bird droppings, hot spots, etc. The timely and accurate detection of these defects can effectively ensure the stability of photovoltaic systems, improve their power generation efficiency, and extend the service life of equipment. However, the defect detection of photovoltaic panels faces multiple challenges [3]. Firstly, photovoltaic panels have diverse types of defects, and the shapes, sizes, and colorsof various defects vary greatly, which makes it difficult for traditional detection methods to achieve precise identification. Secondly, the surface of photovoltaic panels is often covered with substances such as dust and bird droppings, and these environmental interference factors can lead to errors in the detection results. Moreover, traditional image processing methods and deep learning methods often fail to achieve satisfactory results when detecting small-sized defects. Finally, photovoltaic systems are mostly deployed in remote areas with limited resources, which requires the detection model not only to have high accuracy but also to be lightweight to adapt to the resource limitations of on-site equipment [4]. The current methods for detecting defects on photovoltaic panels can be classified into two main categories: traditional image processing methods and those based on deep learning. Traditional methods, such as the K-Means clustering algorithm, the algorithm combining HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine) [5], etc., were widely applied in the early detection of photovoltaic panel defects. These methods achieved the analysis of photovoltaic panel images through manually designed feature extractors. Although these methods were simple and easy to use, they had significant limitations when dealing with complex backgrounds, different scales, and different types of defects [6]. For instance, in the defect detection of photovoltaic panels, since the defect targets are relatively small, after downsampling, the information is further compressed, resulting in significant loss of small target information on the low-resolution feature map, and the phenomena of false detection and missed detection are very serious. CNN-based methods: With the development of deep learning technology [7], CNN (the convolutional neural network) has gradually become the mainstream for detecting defects in photovoltaic panels. Deep learning models such as the YOLO series and Faster R-CNN have performed well in image classification and object detection tasks, especially in the detection of large-sized and obvious defects with high accuracy. Although the YOLO series models are fast [8], they have poor robustness for small-sized defects and complex backgrounds. DETR model: The DETR model achieves end-to-end object detection by integrating the Transformer network [9]. Although the DETR model has certain advantages in handling global context information, its computational complexity is extremely high, making it difficult to deploy on devices with limited computing resources. Moreover, the training process of DETR is relatively complex, requiring a large amount of computing resources, and its real-time detection performance is poor. Regarding the aforementioned issues, this study proposes an improved photovoltaic panel defect detection method, EER-DETR, based on RT-DETR.