# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 2 Related Works
This approach highlighted the potential of multi-sensor data fusion for soil mapping and precision agriculture, particularly in northeastern Tunisia. However, while this methodology offers valuable insights into soil attribute prediction using satellite data fusion, it has limitations related to its scope, complexity, and potential applicability to a broader range of soil properties and geographic regions. Methods for inspecting welding bead quality using image data can be broadly categorized into image segmentation methods and machine learning (ML) methods. Image segmentation involves dividing an image into units at the pixel level to differentiate objects. Among these methods, geodesic active contour segments objects by slowly progressing along curves driven via edge components at the boundaries of the region of interest [9; 10]. The morphology snake method reliably and quickly segments objects by performing morphology operations [11]. Recently, methods for welding bead segmentation using the morphological geodesic active contour (MorphGAC) algorithm have been introduced [12]. However, due to changes in the specific image's location or its susceptibility to variations in light intensity and resolution, achieving complete image segmentation is challenging, resulting in a significant measurement error. [PERSON] et al. proposed a segmentation method using the morphological geodesic active contour algorithm with histogram equalization to normalize the distribution of welding bead images [13]. One drawback of the morphological geodesic active contour algorithm is that it requires an appropriate adjustment of area parameters when creating bounding boxes for segmentation. However, using the same parameters for all welding bead images may lead to the inaccurate detection of welding beads. The Grabcut method is an image segmentation algorithm that extracts the foreground image rather than the background from an image. Recently, research on automatic mask generation using Grabcut and algorithms based on Mask R-CNN and Grabcut for segmenting cancer areas in images has been published [14; 15]. However, the accuracy of these methods may decrease when dealing with complex backgrounds or objectsthat are similar to the background. Additionally, they require repetitive tasks, resulting in longer computation times. Therefore, in welding bead inspection, the similarity in color between the base material and the welding bead can lead to reduced detection performance. Recently, welding bead image inspection methods based on the image projection technique have been proposed [16]. This method uses image projection to obtain pixel values and draws an image histogram to find the \(x\)-axis value corresponding to a specific height. Then, it detects the welding bead's area by cutting based on the \(x\)-value at that location in the region of interest (ROI). This inspection method is faster compared to other image inspections, as it examines only the ROI area, and offers high accuracy by inspecting the brightness values of all ROI regions. In studies employing deep learning (DL), various methods, such as KNN and K-means, have been introduced. KNN is an algorithm that classifies data into clusters based on their similarity by measuring distances. Recently, research has been published on the use of S-KNN for detecting the regions of interest, background, and ambiguous areas, as well as segmenting images of crop leaf data using KNN and histograms [17; 18; 19]. However, KNN is computationally intensive, making it challenging to apply in real-time quality inspection. K-means is an algorithm that groups data with similar features into k clusters, widely used in image segmentation. Recent research has been published on image segmentation algorithms combining adaptive K-means and Grabcut, as well as studies on image segmentation in RGB and HSV color spaces [20; 21; 22; 23; 24; 25]. Nevertheless, K-means requires users to determine the number of clusters in advance, and results can vary depending on the chosen number of clusters.