# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Visible Light
#### 3.1.1 Self-Blast Glass Insulator Location
Considering the characteristics contained in images, researchers have explored color and structural features to detect self-blast glass insulators. On the basis of spatial features, [PERSON] et al. [63] propose a method to specifically deal with the defect detection problems of twin insulator strings. According to the particular color features of glass insulators, the images are segmented, and the main axes of the insulator strings are adjusted to the horizontal direction. After marking the connected regions of insulators, spatial features including the vertical lengths of the regions, the number of insulator pixels in the regions, and the horizontal distances between two adjacent connected regions are selected. In the end, insulators with defects are located and detected based on the spatial features. However, when the insulator area is severely obstructed, it is impossible to obtain an independent connected region based on the insulator umbrella skirt, leading to poor performance of the method. In addition to the self-contained attributes of images, several other characteristics, such as histogram, can also be explored for self-blast glass insulator location. [PERSON] et al. [64] proposed an approach to locate and identify the self-shattering of glass insulators based on local binary pattern histograms. This method also follows the location and detection strategy. In order to reduce the interference of illumination, the color space is converted from RGB to HSI. Next, insulator strings are separated from the complex backgrounds, after segmenting the image using the maximum interclass variance method (OTSU). Then, local binary pattern histograms on sliding blocks are employed for self-blast detection. Instead of solving the problem in the original image domain, the digital image processing (DIP) technique can be utilized to tackle the self-blast glass insulator location from another domain. [PERSON] et al. [65] transform the original image to the complex frequency domain using discrete orthogonal S transform (DOST). The pixel classification technique and k-means clustering are used to segment the image into various clusters first, and then a bounding box is drawn to detect the insulator. After cropping the original image along the edge of each box, DOST is applied, and features are extracted in the complex frequency domain. Finally, SVM and an adaptive neuro-fuzzy inference system (ANFIS) is applied to determine the condition of the insulator. The complex frequency domain transformation could bring extra computation complexity, leading to high computation costs in real-world situations. In addition to DOST, wavelets analysis combined with SVM [66] or hidden Markov model (HMM) [67] can also be applied for insulator condition analysis. Though classical methods have achieved great self-blast glass insulator detection results, these methods rely heavily on prior knowledge, such as the insulator shape, size, and background. However, in the real world, insulators are quite different and their backgrounds vary significantly, from farmlands to vegetation, rivers, mountains, and towers. In addition, the hand-crafted features are low level and limited, so they are not capable of dealing with various situations. Deep Learning Methods: Recent years have witnessed the remarkable development of deep learning methods, especially convolutional neural networks (CNN), in computer vision. Compared with hand-crafted features, one can extract more discriminative, generalized, and robust high-level feature representations using CNN. Driven by the large-scale image data of smart grids, researchers have started to apply deep learning methods to self-blast glass insulator identification tasks. The first example of applying CNN architecture to the detection of power line insulator defects was proposed by [PERSON] et al. [46] and is composed of three steps. The first step is insulator detection, aiming to localize insulators with various angles.