# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
###### Abstract
The multi-correlation filter was implemented in real-time to detect the weld seam and position the weld torch, and the tracking error was found to be less than 1 mm for straight and curved weld seams [10]. [PERSON] et al. study the problem of domain generalization in object detection using sample reweighting method called region-aware proposal reweighting (RAPT). Extensive experiments were conducted on different datasets and various architectures [11]. According to the discussion above, there has been limited study on identifying weld seam shapes, which will be useful when the robot is unable to detect the welding area and can also be utilized as a first step in autonomous robotic welding. There are limited datasets for the welding process, particularly datasets with different shapes; hence, an attempt has been made to create datasets similar to the industrial process for weld shape detection. Additionally, major works on image denoising using GAN were tested on public datasets, and very few works were applied for custom datasets. So, GAN was used for weld fume removal in the current literature by applying image-to-image translation. Additionally, many authors used an expensive real-time experimental setup, including lasers and CCD cameras. As a result, a look-alike industrial welding setup has been demonstrated utilizing a simple 2D camera with two phases--dehazing using DW-GAN and a YOLO-based object detector--which is the major contribution of this work. Initially, mild steel and aluminium weld plates are recognized using several YOLO algorithms. If the image contains weld fumes, it is transferred to the dehazing step, where the fumes are removed using discrete wavelet-based GAN and dark channel prior methods. Further ablation studies and generalisation have been carried out for the deep learning models used in this work. After removing the fumes, the weld seam is retrieved using contour detection with binary thresholding, and its pixel coordinates are recorded as.csv files for subsequent processing. A neural network technique is applied to simplify the coordinate transition between weld contour locations and the robot in order to execute real-time robotic welding. For tracing the weld seam, the five DOF robot receives the robot coordinates obtained from the neural network model. The following are the key goals of this paper:Create a real-time weld dataset made up of actual weld plates with a variety of different weld shapes;
2 Remove weld fumes using the DW-GAN;
3 Train the dataset with genetic algorithm-based different YOLO approaches, such as Scaled YOLOv4, YOLOv5, and YOLOv4 DarkNet;
4 Determine the contours of various weld seam shapes using image processing;
5 Convert pixel coordinates to robot coordinates using a Backpropagation Neural Network model;
6 Perform real-time weld seam detection for tracing the weld seam shapes using a live 2D camera. The paper is divided into five sections. Section 2 describes the GAN-based dehazing and object detection of the weld seam using different YOLO algorithms. This is followed by a discussion of the performance metrics used for weld seam detection in Section 3. The real-time experimentation results are discussed in Section 4, followed by the conclusion and future scope in Section 5.