Applications of Machine Learning in Computer Vision: A Review

Abstract

Machine Learning (ML) has become an important aspect of computer vision due to the increased efficiency, scalability and high accuracy in image recognition, object detection and classification. This work examines the performance of four chosen ML techniques CNNs, SVMs, RFs, and KNNs to a variety of visual tasks in healthcare, manufacturing, agriculture and environment surveillance. To assess the performance of these algorithms, accuracy, precision, recall, and the F1-score were conducted on the foundation of a strong data set of 50,000 annotated images. As it is shown the highest accuracy of the algorithm is 97.8%, which belongs to CNN while the lowest accuracy is 88.9% which is associated with KNN, while SVM and RF showed the accuracy of 92.4% and 90.6%% correspondingly. Another outstanding feature was higher accuracy of CNN; 98.2%, and quality, or recall; 96.9 %; thus CNN can be used in medical imaging, robotic quality control, etc. The comparative analysis with the related work showed noticeable enhancement in the efficiency and reliability; hence, the practicality of using ML in the real-life applications was also verified. However, there were challenges highlighted for future work including the computational requirement for such analyses, as well as ethical issues. This work also underscores the possibilities of ML for change in computer vision and opens new avenues in critical fields.