# A Comprehensive Survey for Deep-Learning-Based
## 2 Related Works on Abnormality Detection Based on Multimodal Image Data  
Visible Light Images: With the striking advancements in image processing, visible light has been brought to the forefront of research for abnormality detection in smart grids [9; 10; 11; 12; 13; 14]. Visible light modality [15; 16; 17; 18] means utilizing cameras mounted on various devices to detect transmission line abnormality. Existing methods for collecting visible light images mainly include ground patrol, fixed camera, climbing robot, helicopter, and unmanned aerial vehicle (UAV). Ground patrol is one of the more traditional methods, with humans traveling on foot, which is very slow and labor intensive. In addition, being subject to the influence of extreme weather and harsh terrain, this method is not always possible. Fixed camera is another choice for data collection, but its deployment has a high cost due to the vast number of transmission lines. A climbing robot can achieve high inspection accuracy because of its proximity to the power lines, but its weight may damage the cables. As aerial platforms such as helicopters and UAVs have become available in recent years, their high accuracy and efficiency have made aerial images the central data resource for abnormality detection in smart grids. **Infrared Images:** Energy utilization [19; 20; 21; 22] currently plays a vital role in the development of power systems and smart grids. Therefore, proper monitoring and control of energy consumption is the primary task of the smart grid. For an abnormal power grid, as detected by energy equipment, we summarize three different methods to detect and solve these abnormalities. The equipment in the power grid environment will inevitably have faults and abnormalities in its operation process. For example, pieces of equipment with different functions (such as transformer winding, insulated current transformer, high-voltage disconnector, and watt-hour meter system) have a certain probability of abnormality during operation. Therefore, many detection technologies have been proposed to perform abnormality detection for power grid equipment with different functions. In this paper, we divide the power grid abnormality detection methods into three categories: (1) _Multi-stage:_ A method was introduced by [23] that uses a low-voltage pulse test to check the condition of transformer winding and the infrared control results of electrical equipment; checks the insulated current transformer through thermal and visual control; and measures the acoustic activity of partial discharge in transformer insulation, the gas pressure in the shell, and the data of electrical tests to comprehensively analyze whether there are abnormalities [24; 25] in electrical equipment. (2) _Multi-sensor:_ A method was introduced by [26] that uses an autonomous, wireless multi-variable intelligent sensor [27; 28; 29; 30; 31], which can collect and detect the position of abnormal remote-operated high-voltage disconnectors and use the integrated infrared equipment to detect the temperature data of abnormal switches. (3) _Multi-output:_ The methods in [32; 33; 34] use an intelligent watt-hour meter based on the Internet of things (IoT) [35; 36; 37; 38], which is proposed to properly monitor and control the energy consumption of the watt-hour meter system. The system can accurately control and calculate the energy consumption and upload it to the cloud. It can also accurately detect an abnormal watt-hour meter system, which is helpful in detecting power theft. Visible light/infrared images enable a solution at a distance of several meters, while optical satellite images can be obtained at a distance of thousands of miles. Optical Satellite Images: Optical satellite images are better understood by humans because they are in the visible light band. Therefore, by understanding the content of the image, we can deal with various abnormal detection problems in the power grid environment. For example, [PERSON] et al. [9] detect towers and conductors by using very high-resolution satellite images.