# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 1 Introduction
In Reference [19], the authors came up with an approach for segmentation of the reflected laser lines for pulsed gas metal arc weld (GMAW-P). In References [9; 20], the reflected laser lines from the GMAW-P weld pool were segmented manually to measure the weld pool oscillation frequency. In Reference [21], two cameras were used to measure the shape of the weld pool for GMAW-P and an unsupervised approach was proposed to segment and cluster the reflected laser lines. In Reference [22], three cameras are used to measure the specular shape from the projected laser rays and an unsupervised approach was proposed to reconstruct the GTAW weld pool shape with closed form solutions. It achieved on line robust measurement of GTAW weld pool shape with three calibrated cameras. There is one major difference for the used laser pattern among these structured light methods [9; 15; 16; 17; 18; 19; 20; 21; 22; 23]. The laser dot pattern is used for the measurement of the GTAW weld pool surface while the laser line pattern is used for the measurement of the GMAW weld pool surface. Compared to the GTAW weld pool surface, the GMAW weld pool surface is much more dynamic and fluctuating, because GMAW process transfers additional metallic and liquid droplets into the weld pool, which increases the fluctuation and dynamics of the pool surface greatly. The position and geometry of the local specular surface changes rapidly and greatly, which causes the reflected rays to change their trajectories rapidly and greatly. If the laser dot pattern is used, the reflected laser dots might interlace irregularly, which makes it impossible for the unsupervised clustering method to identify these dots robustly. Therefore, the laser line patterns are used in measuring the weld pool surface of the GMAW process [19; 20; 21]. Robust segmentation and clustering of the reflected laser lines become the most important and challenging part in the whole monitoring system, because of the uncertainty of the weld pool geometry. The quality of the captured image is significantly affected by the welding parameters. Due to the lack of generality and robustness, the proposed image processing methods might work for the images captured in some specifically designed welding experiments,while might not work for the images captured in other experiments. For instance, the reflected laser lines were filtered by the top-hat transform and then segmented by thresholding in Reference [19]. It yields acceptable segmentation results in many cases. However, it fails completely when it is used to segment the images captured in Reference [21] with different welding parameters. The reflected laser lines were filtered by the fast Fourier transform (FFT) and then segmented by a manually specified threshold in Reference [20]. It also fails completely in segmenting the images shown in Reference [21]. To segment the reflected laser lines more robustly, a difference method and an effective threshold selection method are proposed in Reference [21]. The segmented laser lines were then clustered based on their slopes. One big problem that could be seen from the experimental figures in References [19; 20; 21] is that quite a few of the laser line parts are missing, because of the limitations of their proposed image processing methods. One direct consequence of missing a significant part of the reflected laser line is the inaccurate characterization of the weld pool shape, since the length of the reflected laser is proportional to the size of the weld pool. Another drawback is that the segmented small part of the laser line might be deleted as noise blobs or some large noise blobs might be recognized as part of the reflected laser line during the unsupervised clustering. As a result, the laser line might be clustered incorrectly. In Reference [23], a new approach was proposed, and it achieved significantly better segmentation accuracy compared to the past research [19; 20; 21].