# An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm
###### Abstract
The heated area can be effectively segmented, and the hot spots were presented in a clear view. By extracting features from the visualized heated area, the severity of the heated part affecting the segmented binary image was demonstrated, and the visual effects were enhanced. In order to automatically identify the defects forming hot spots in solar photovoltaic systems, [PERSON] et al. [11] proposed a machine learning technique, combining K-Means color quantization and DBSCAN processing, to detect and separate the hot spot areas from the observed PV module infrared images under outdoor conditions. Applied to hot spot datasets, it was concluded by the proposed method that the hot spot area is related to the decrease in photovoltaic module efficiency. From the above-mentioned review, it can be seen that the traditional hot spot segmentation algorithm methods are time-consuming and inefficient, though the detection accuracy can reach a certain extent by image feature extraction with human involvement. In recent years, with the continuous development of deep learning, the neural network algorithms for automatic segmentation of hot spots have been investigated by many more scholars around the world. [PERSON] et al. [12] proposed a novel PV panel condition monitoring and fault diagnosis technique in which a well-trained U-Net neural network and decision tree were combined and the infrared thermal images of the PV panel were intelligently analyzed. The research results show that PV panel faults can be diagnosed with 99.8% accuracy using the proposed method. [PERSON] et al. [13] developed a deep learning artificial intelligence system based on the Mask R-CNN [14] architecture for detecting faults from photovoltaic thermal images. The Mask R-CNN architecture can be simultaneously used for object detection and instance segmentation and is beneficial for accomplishing intended detection tasks automatically. The developed method was trained and evaluated with a photovoltaic thermal image dataset, and the experimental results demonstrated that photovoltaic power modules can be diagnosed effectively through thermal imaging. To improve the power generation efficiency of PV systems and ensure power stations' safe and stable operation, [PERSON] et al. (2015) proposed a novel method for detecting hot spots of PV panels based on improved anchors and prediction heads of the YOLOv5 (AP-YOLOv5) network, by which the mean average precision (mAP) can achieve 87.8%, the average recall rate and the F1 score can reach 89.0% and 88.9%, respectively. [PERSON] et al. (2016) proposed a novel system for improved PV diagnostics using a region-based convolutional neural network (CNN) based on drone-based imagery, in which a true positive rate or recall of more than 90% for a false positive rate of around 2% to 3% was achieved on a dataset containing nearly 9000 solar panels. Recently, the networks used for image segmentation have been mainly divided into two types. One is the SegNet and U-Net semantic segmentation methods obtained from the evolution of FCN, and the other is the Mask R-CNN instance segmentation algorithm obtained from the improvement of R-CNN. Both SegNet and U-Net are an encoder-decoder network structure whose several down-sampling and up-sampling structural results in the loss of edge information when performing hot spots segmentation. Mask R-CNN is a deep learning framework for segmenting and recognizing multiple targets simultaneously with high accuracy by using labeled images for training. Especially in recent years, different researchers have improved and optimized the Mask R-CNN algorithm according to their needs, and the improved Mask R-CNN has performed well in distribution power lines ([PERSON] et al., 2014), composite insulator strings ([PERSON] et al., 2014), medicine ([PERSON] et al., 2014), construction ([PERSON] et al., 2014), etc. After the above analysis, the network Mask R-CNN plays an important role in target segmentation with powerful segmentation and recognition capabilities and low hardware requirements.