Real-Time Object Detection and Associated Hardware Accelerators Targeting Autonomous Vehicles
Abstract
The efficiency of object detectors depends on factors like detection accuracy, processing time, and computational resources. Processing time is crucial for real-time applications, particularly for autonomous vehicles (AVs), where instantaneous responses are vital for safety. This review paper provides a concise yet comprehensive survey of real-time object detection (OD) algorithms for autonomous cars delving into their hardware accelerators (HAs). Non-neural network-based algorithms, which use statistical image processing, have been entirely substituted by AI algorithms, such as different models of convolutional neural networks (CNNs). Their intrinsically parallel features led them to be deployable into edge-based HAs of various types, where GPUs and, to a lesser extent, ASIC (application-specific integrated circuit) remain the most widely used. Throughputs of hundreds of frames/s (fps) could be reached; however, handling object detection for all the cameras available in a typical AV requires further hardware and algorithmic improvements. The intensive competition between AV providers prevented the disclosure of associated algorithms, firmware, and sometimes even hardware platform details. This constitutes a hurdle for researchers as commercial systems offer valuable input to researchers as they go through intensive and time-consuming training and tests on different roads. Consequently, thousands of research papers on AVs may not be endorsed in the end products as they were mainly developed based on existing datasets in limited situations. This paper highlights the state-of-the-art algorithms used for OD and intends to narrow the gap with technologies used in commercially available AV. To our knowledge, this aspect was not addressed in previous survey papers. Therefore, this paper can be a tangible reference for researchers designing future generations of vehicles, which are expected to be fully autonomous for comfort and safety.