Comparative Performance Study of CNN-based Algorithms and YOLO

Abstract

Tasks such as image classification, object detection, to mention a few, play an important role in computer vision. Numerous algorithms have been developed to improve the performance of such tasks for benchmark datasets. Although advanced algorithms offer state-of-the-art performance on such tasks, it is also important to analyze their algorithmic feasibility over the time to make it practical for end-user applications. This paper analyzes two such groups of algorithms, namely, Convolutional Neural Networks (CNN) based algorithms with You Only Look Once (YOLO) in terms of speed and accuracy.