# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 2 Related Work  
### Defect Detection of Photovoltaic Panels  
In the early days, the defect detection of photovoltaic panels mainly relied on manual visual inspection and simple image processing techniques. Although manual visual inspection could detect obvious defects in some cases, it required highly skilled workers and was inefficient and susceptible to human interference. Meanwhile, traditional image processing techniques such as edge detection, threshold segmentation, and region growing were also applied to detect the defects on photovoltaic panels. These methods determined the defect location by detecting abnormal features or irregular shapes on the surface of the photovoltaic panels. However, these methods often failed to handle complex backgrounds, changes in lighting conditions, and noise interference, thus limiting their detection accuracy and stability, and their performance was relatively slow when dealing with large-scale images. With the rapid development of computer vision and machine learning technologies, the research on photovoltaic panel defect detection has gradually shifted from traditional image processing methods to data-driven machine learning algorithms. Particularly, from the late 20 th century to the early 21 st century, traditional machine learning algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) began to be applied to photovoltaic panel defect detection [10]. SVM can classify different types of defects with relatively high accuracy by constructing a decision hyperplane in a high-dimensional space, while the K-NN algorithm classifies samples by calculating the distance between them. However, these traditional machine learning algorithms still face problems such as low accuracy and insufficient robustness to noise when dealing with high-dimensional feature data and complex patterns. For example, the training and parameter tuning process of SVM in high-dimensional space is relatively complex, and it is sensitive to the uneven distribution of data between classes. Despite these issues, these methods laid the foundation for the later application of deep learning techniques in photovoltaic panel defect detection and provided valuable experience for improving detection efficiency and accuracy. Deep learning-based computer vision techniques, applied to images of solar panels, offer the potential of automated defect detection, providing a reliable, cost-effective, and non-invasive solution for solar panel inspection [11; 12; 13; 14]. Faster R-CNN [15] (proposed by [PERSON] et al. in 2015) is a deep learning-based object detection method that utilizes RPN (Region Proposal Network) to generate candidate regions and combines them with a target detection network. Unlike traditional methods that rely on selective search to generate candidate regions, RPN directly generates anchor boxes on convolutional feature maps and achieves significant improvements in the speed and accuracy of object detection through end-to-end training. Faster R-CNN eliminates redundant steps in candidate region generation by sharing convolutional features and optimizes the detection process. Its training is end to end, with RPN and the target detection network jointly optimized to enhance detection accuracy. [PERSON] et al. proposed the YOLO (You Only Look Once) algorithm, which transformed the object detection problem into a regression problem and significantly improved the detection speed [16]. Subsequently, the YOLO series has been continuously iterated and upgraded, including versions, such as YOLOv2, YOLOv3, YOLOv4, YOLOv5, and YOLOv6. [PERSON] et al. improved the accuracy and robustness of photovoltaic panel defect detection by modifying the loss function and attention mechanism based on the YOLOv5 model [17]. The latest YOLOv8 model has achieved new heights in both speed and accuracy.