Empirical Performance Analysis of Conventional Deep Learning Models for Recognition of Objects in 2-D Images

Object detection is an elementary Computer Vision task which deals with the classification of objects in a digital image to a particular class (such as airplanes, cars, humans, etc). This can further be used in the implementation of real-world systems like face detection, pedestrian detection, automated driving systems, video surveillance, among other applications. It has gathered a lot of attention in the last few years since it is closely related to video analysis. They also help provide keen insights on the image contents. In recent years, deep learning methods have gained momentum and are now able to learn large amount of features, at comparatively deeper levels, and are thus able to address the problems faced earlier in traditional network architectures, such as artificial neural networks.