# Buckle Pose Estimation Using a Generative Adversarial Network
## 2 Related Work
### Traditional Detection Approaches  
Traditional methods require manually labeled features designed for specific conditions and classifiers or template matching methods to detect targets using these features. The algorithms enumerate all possible targets in input images by combining several classical methods with parametric fine-tuning. Examples include cascade classification [8], sparse Fourier transformation [9], histogram of oriented gradient edge shape characteristic description [10], deformable part detection based on components [11], Haar wavelet characterization, and support vector machine (SVM) prediction [12]. [PERSON] et al. [13] proposed a crack extraction method that used multiscale morphological operations for connector crack detection. However, it was susceptible to changes in background brightness. [PERSON] et al. [14] used a double-threshold method and gear involute geometric relationship to determine the tooth pitch in a local image of a gear by reversely mapping the filtered pixel information to the base circle and calculating the phase angle. However, this measurement method was susceptible to data diversity problems. Meanwhile, [PERSON] et al. [15] proposed a method to detect train center plate bolts. Gabor wavelets of different scales were used to act on the image, the weight obtained by each channel was optimized by a genetic algorithm, and the weighted features were classified using SVM. However, the requirements for manual design features relied too much on human subjectivity and, the method lacked generalizability.  
Owing to the characteristics of large background interference and diverse buckle shape data, it is necessary to analyze the characteristics of each buckle using traditional methods. Moreover, the recognition accuracy depends largely on the feature generation process, which limits their applicability to industrial applications. Therefore, this study compares traditional binarization and segmentation methods. Binarization methods can be  
Figure 1: Different buckle types (buckles are inside the red box).  
divided into local and global categories. Global methods use only one threshold over the entire image, whereas local methods use multiple regional thresholds. We considered two contemporary methods, those developed by [PERSON] [16] and [PERSON] [17], for their global and local thresholds, respectively. Furthermore, we used the watershed segmentation algorithm [18] to segment images into disjointed regions that are related in terms of certain attributes. The results of these three methods are shown in Figure 2.  
The results shown in Figure 2 indicate that traditional methods perform poorly in data extraction. In [PERSON]'s method, although a rough outline of the target can be regressed, it is very difficult to find the target position and estimate the pose. The watershed method eliminates the influence of the background, but it is highly sensitive to noise; hence, much of the valuable target information is lost. [PERSON]'s method treats parts of the background as foreground, resulting in a complete loss of the target information.