Gloves defect detection using computer Vision In MATLAB

Abstract—

MATLABSolutions demonstrate In this task we are going to design goal of this research is to create a computer software that can precisely identify glove flaws using image processing methods. Small cuts, large cuts, and no flaws are the three categories of faults that the software is supposed to find and classify. To do this, we constructed a test script that we then used to apply a variety of image processing techniques, including thresholding, morphological operations, and region attributes like area, perimeter, major axis, and minor axis. We were able to precisely identify and classify the glove defect from these processes. The images were then pre-processed to enhance their quality through the use of colour correction or grayscale conversion. After that, we properly identified and categorized the problems using a variety of image processing techniques. We employed a variety of techniques, including morphological operations, filling gaps, and thresholding. The GUI that the application uses to present the defect in the input image with the processed image and displays the identified fault class at the bottom. To identify just the imperfections or flaws in the hand glove photos, we created a number of methods for segmenting, filtering, and masking the RGB input image. The successful development of this software serves as evidence of how well image processing methods can identify glove flaws. It also demonstrates the possibility for incorporating such systems into current manufacturing lines to guarantee product safety and quality while cutting down on time and expense.

Introduction—

In this project we are going to develop the computer program which will detect the defect in the gloves for the three categories small cut, big cuts and No defects in the gloves using image processing, we first create the test script on which we apply various image processing operation such as morphological, thresholding and region properties like area, perimeter, major axis, minor axis etc. It's crucial to find glove flaws to guarantee the product's safety and quality. By taking high-resolution pictures of the gloves and using different image processing methods to find any problems, image processing techniques may be utilized to find flaws in gloves. Pre-processing, flaw identification, categorization, and output production are some of the processes in the process. Getting high-quality pictures of the gloves is the first step. A high-resolution camera may be used for this to take pictures from various angles and viewpoints. After that, the photographs are pre-processed to improve their quality through the use of colour correction or by turning colour space into grayscale. The following stage is to find flaws using image processing methods. Depending on the kind of faults being found, many procedures can be utilized, including thresholding, filling holes, and morphological processes. Finally, the output is displayed on the figure with input image and corrected image by comparing side by side. Lastly, we will create the GUI which will select the glove image and based on the process it will show the results on GUI.

Image processing is essential for finding glove flaws. It entails analysing digital photographs of gloves to look for any imperfections or faults using a variety of algorithms and approaches. Techniques for image processing may be used to find various glove flaws, including holes, rips, splits, and punctures. The fact that image processing is extremely exact and objective makes it one of the main advantages for glove detection. In contrast to manual inspection, which is subject to human mistake, image processing can swiftly and reliably examine the photos of gloves. The greatest level of product quality is ensured since it can spot even little flaws that a manual examination could overlook. The photos are pre-processed using image processing techniques in order to spot faults, classify them, and show the results on a GUI. Pre-processing entails increasing the image quality by taking out noise, fixing colour, and boosting contrast. This makes it more likely that the flaws will be correctly and visibly evident.