The defect detection of photovoltaic modules based on temperature infrared image is an important technology to realize the large-scale modules quality detection of photovoltaic power plant. In this paper, the causes and hazards of hot spot of photovoltaic modules are briefly introduced. The artificial neural network model and its performance of infrared image and video of photovoltaic modules are summarized and compared from three aspects: hot spot detection, hot spot location and extraction. The hot spot detection accuracy of the improved YOLOv5 model for photovoltaic modules reached 98.8%, and the hot spot positioning accuracy of the Lucas–Kanade sparse optical flow algorithm reached 97.5%. At the end of this paper, the development trend of hot spot detection technology adapted to meet the operation and maintenance needs of large-scale photovoltaic power plant is briefly discussed.