As a Data Analyst at ABC Corp., I recently led a project where we used SVM to tackle an image classification task. The challenge was to identify defects in manufactured components based on high-resolution images. We opted for SVM due to its ability to handle high-dimensional data and its adaptability through the choice of different kernels.
Our first step was preprocessing the images and extracting relevant features. We then trained our SVM model using Radial Basis Function (RBF) kernel on the preprocessed data and used OpenCV for image processing and scikit-learn for implementing the SVM model.
The model was successful at distinguishing defective and non-defective parts with roughly 93% accuracy. This significantly accelerated our defect detection process, leading to improved quality control measures in the production process and increased customer satisfaction.