# 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  
As essential electrical insulation and conductor conjunction equipment, insulators are widely used in modern high-voltage power transmission lines. According to statistics, accidents caused by insulator fault (e.g., glass insulators self-blast) account for the highest proportion of grid system failures [70]. Thus, it is of great importance to conduct periodic abnormality detection of insulators. Typically, the self-blast glass insulator location basically consists of two steps: (1) insulator localization and (2) fault identification. First of all, one needs to localize the insulators by eliminating complicated backgrounds, dynamic view, and illumination changes of the images. Then, the conditions of the insulators need to be diagnosed to determine whether they are normal or defective. Classical methods: As discussed above, the two-step strategy locates the insulator first and then extracts the features for further classification; that is, the classifier is used to determine whether the detected insulator is normal or self-blast. Considering the various characteristics of the image, classical statistic features (such as mean, contrast, variance, histogram, color, structure, and so on) are used for feature representation. Some works transform the image to the frequency domain or complex frequency domain using Wavelet [66] and S-Transform [65] for feature extraction. Classification algorithms such as k-means [77], support vector machine (SVM) [65], and other task-specific methods are applied for insulator self-blast recognition. Many works have been done on insulator localization and status recognition using classical methods. [PERSON] et al. [61] proposed a robust insulator detection algorithm based on local features and spatial orders for aerial images. First, the local features are detected, and a multi-scale and multi-feature (MSMF) descriptor is presented to represent the local features. Next, several spatial order features are obtained by training the local features. Finally, a coarse-to-fine matching strategy is utilized to eliminate the background noise and locate the insulators. [PERSON] et al. [62] introduced a texture segmentation algorithm \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scenario** & **Objective** & **Technique Taxonomy** & **Technique** & **Literature** \\ \hline \hline \multirow{6}{*}{Locate and identify self-blast glass insulator} & \multirow{6}{*}{Locate and identify self-blast glass insulator} & \multirow{6}{*}{Classical methods} & \multirow{6}{*}{MSMF descriptor to extract features, k-means algorithm to generate visual vocabulary of insulator based on local features and spatial orders, coarse-to-fine matching strategy for identification} & \multirow{6}{*}{[61]} \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \cline{3-3} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Visible-light-based abnormality detection. for aerial insulator images, which is based on principal component analysis (PCA) and global minimization active contour model (GMAC). First, the features of insulators are extracted by the gray level co-occurrence matrix (GLCM). After dividing the extracted features into those with weaker and stronger discriminative abilities, PCA is applied to features with weaker abilities to optimize the feature representation. Finally, GMAC is utilized for insulator segmentation.