  Object detection is the identification of an object in the image along with
its localisation and classification. It has wide spread applications and is a
critical component for vision based software systems. This paper seeks to
perform a rigorous survey of modern object detection algorithms that use deep
learning. As part of the survey, the topics explored include various
algorithms, quality metrics, speed/size trade offs and training methodologies.
This paper focuses on the two types of object detection algorithms- the SSD
class of single step detectors and the Faster R-CNN class of two step
detectors. Techniques to construct detectors that are portable and fast on low
powered devices are also addressed by exploring new lightweight convolutional
base architectures. Ultimately, a rigorous review of the strengths and
weaknesses of each detector leads us to the present state of the art.
