A Review of Recent Advances in Deep Learning for Object Detection

Abstract

Computer Vision has given a way for computer systems to see by way of deciphering the surrounding objects, which has been considered a crucial problem over the years. There are quite a number of techniques devised for object detection and deep learning, but most of the research has been focused on deep learning in recent years. Visual object detection covers numerous recognition pattern tasks like image classification. This article aims to review a visual analytics approach for better understanding, identifying, and cleansing object detection frameworks and the effects of some factors such as sampling strategies, feature learning, detector architectures, proposal generation, etc., on visual object detection with special reference to detection components, learning strategies, and their applications along with benchmarks.