A Comprehensive Review on Object Detection in the Context of Autonomous Driving

In autonomous driving, safety assessment is becoming an essential component, particularly in the perception and analysis of the environment around them. To make safe driving judgments, autonomous cars mostly rely on their capacity to observe and comprehend their environment. Detecting objects is a frequent and challenging problem in autonomous driving. Over the course of the last ten years, as deep learning has rapidly advanced, researchers have conducted numerous experiments and made significant contributions to the performance improvement of object recognition and related tasks including object categorization, localization, and segmentation. In this paper, an overview of state-of-the-art object detection algorithms is presented with in depth analysis. Region proposal-based approaches and single-shot approaches are two main frameworks of object detection which are discussed here. R-CNN, SPP-net, Fast R-CNN, and Faster R-CNN refer to region proposal-based methods while single-shot methods consist of YOLOv2, YOLOv3, YOLOv4 and SSD. The authors truly believe that this study would have a meaningful impact on the current advances in object detection algorithms.