FPGA-based accelerator for object detection: a comprehensive survey

Object detection is one of the most challenging tasks in computer vision. With the advances in semiconductor devices and chip technology, hardware accelerators have been widely used. Field-programmable gate arrays (FPGAs) are a highly flexible hardware platform that allows customized reconfiguration of the integrated circuit, which has the potential to improve the efficiency of object detection accelerators. However, few reviews summarize FPGA-based object detection accelerators. Also, there is no general principle for realizing object detection according to FPGA characteristics. In this paper, the current hardware accelerators are introduced and compared. Then, the typical deep learning-based object detectors are summarized. Next, the questions of “Why choose FPGA,” “The design goals of FPGA accelerators” and “The design methods for FPGA accelerators” are discussed in detail. Finally, the challenges of object detection algorithms, hardware, and co-design are presented. In addition, an online platform (https://github.com/vivian13maker/) is constructed to provide specific information on all advanced works.