Analysis of Deep Learning Architectures for Object Detection - A Critical Review

In the research work, we have performed a comparative study on the deep learning architectures in context with object detection. We have selected 4 architectures specifically for the critical comparative analysis and the selection of architectures depend on the optimality and compatabilty. We have carried out experiments on the architectures by tuning the parameters and training them on different datasets to understand their behaviour. The study evidently proves, that the identification of optimal parameters for a network in context with a specific dataset is always independent in nature.