Computational Comparison of Deep Learning Algorithms for Object Detection

Abstract

Finding and recognizing different objects in an image quickly and reliably is an important field of computer vision. Object detection is a challenging problem in this field. Humans have the ability to perform such complex tasks fast and accurately. In contrast, the problem of locating objects via a computer is not so simple. Deep learning algorithms have emerged as powerful methods to detect objects in an image. In this paper, six state-of-the-art object detection algorithms are presented, analysed and compared computationally using four different datasets, two single class and two multiple class datasets. The computational results show that the algorithms achieve higher accuracy on the single class datasets than the multi class datasets.