What role does object detection play in autonomous driving?

Deep learning-based object detection algorithm is becoming more and more important in autonomous driving area with an increasing amount and trending these days. This article first provides definitions and introduces an autonomous driving and object detection. Subsequently, a detailed discussion is conducted on object detection, comparing traditional object detection methods with deep learning object detection algorithms. The shortcomings of traditional methods highlighted the advantages of deep learning-based object detection algorithms, laying the groundwork for the use of deep learning object detection algorithms in the following text. Finally, several detection objects and detection scenarios are introduced. The detection objects are divided into different parts, including moving targets, stationary targets, and infrared targets. Moving targets such as pedestrians and vehicles, while stationary targets include traffic signs and lanes. The detection scenarios are classified into ordinary scene detection and complex scene detection. In the discussion section, the commonly used datasets for autonomous driving target detection training are listed first, such as the KITTI dataset, the COCO dataset, and so on. Subsequently, a discussion was conducted on algorithms, mainly focusing on the models and features in one stage and two stages. For different types of algorithms, this article discusses the advantages and disadvantages of the algorithms. When judging the superiority or inferiority of algorithms, there are usually two aspects: detection accuracy and detection speed. FPS is a commonly used indicator for detection speed, and detection accuracy mainly covers five aspects, namely accuracy, precision, recall, AP (average precision), and MAP (mean average precision). Finally, how the improved algorithms are applied and solve the existing problems is discussed.