# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 1 Introduction
Similarly, an edge detection method in color images was also proposed in [16]. The outstanding improvement of this method is the use of an adaptive median filter to eliminate noise and to apply type 2 fuzzy set-based thresholding to solve the problem of vagueness and uncertainties in gradient images. Therefore, this method achieves better edge detection performance than other traditional algorithms. In recent years, deep learning techniques have been widely applied in the field of image processing. Among these, GAN-based approaches and their potential are increasingly being developed [17]. Furthermore, the application of GAN as an image-to-image translation network helps to solve many problems in computer vision, especially object semantic detection and segmentation [18; 19; 20; 21; 22; 23; 24]. Additionally, many variations of GAN have been introduced, such as CrackGAN [21], DCGAN [22], cycle-GAN [23], URCA-GAN [25], etc. To perform the above tasks, a series of other deep learning networks have also been proposed [26; 27; 28; 29; 30; 31; 32; 33]. Although deep learning models have been used for edge detection in many studies [27; 28; 29; 30], no study on color structured light stripe edge detection has been presented. As mentioned above, the accurate edge detection for color stripe pattern images depends on the quality of the captured images, which is strongly influenced by noise. After studying many previous approaches, we found that GAN-based models can effectively solve the problem of noise reduction in images [34; 35; 36]. As a translator, after being trained with real images and those generated by the generator, the discriminator in GANs can generate images that look like the real ones. Therefore, for detecting edges in the color stripe pattern images with high pixel location accuracy, a color structure light stripe edge detection method based on generative adversarial networks (GANs) is proposed in this paper. In addition to the De Bruijn sequence-based color stripe pattern, a binary multi-slit pattern was also designed and used in the experiments. It should be noted that the slits in the binary multi-slit pattern are located in positions corresponding to the left edges of the color stripes in the stripe pattern. Firstly, the images of objects with the two patterns at each position are respectively captured by the camera. Among them, the images with multi-slitpatterns are thinned and then selected as ground-truth images. After that, the images of a deformed color stripe pattern with the corresponding ground-truth images are sequentially cut into small patches and then divided into the training, validation, and testing sets. After training the model, the test images are predicted by the trained model and then merged together into large images. Finally, the centerlines in the resulting images are detected as color structured light stripe edges. With our experimental results, the proposed method is shown to overcome the above-mentioned challenges and thus detect edges in color stripe images with higher pixel location accuracy than other methods. The rest of this paper is organized as follows. After the problem definition is introduced, the materials and methods, which include our network structure, dataset preparation, training process, and the color structured light stripe edge detection method, are presented in Section 2. After conducting the experiments, the evaluations of the segmentation performance of the proposed network and the pixel location accuracy of the detected edges are presented in Section 3. Finally, the conclusions about the proposed method are given in Section 4.