Name
Abstract | ||
|---|---|---|
We implement the YOLO (You only look once) object detector on an FPGA, which is faster and has higher accuracy. It is based on the convolutional deep neural network (CNN), and it is a dominant part of both the performance and the area. It is widely used in the embedded systems, such as robotics, autonomous driving, security, and drones, all of which require high-performance and low-power consumption. A frame object detection problem consists of two problems: one is a regression problem to spatially separated bounding boxes, the second is the associated classification of the objects within realtime frame rate. We used the binary (1 bit) precision CNN for feature extraction and the half-precision (16 bit) precision CNN for both classification and localization. We implement a pipelined based architecture for the mixed-precision YOLOv2 on the Xilinx Inc. zcu102 board, which has the Xilinx Inc. Zynq Ultrascale+ MPSoC. The implemented object detector archived 35.71 frames per second (FPS), which is faster than the standard video speed (29.9 FPS). Compared with a CPU and a GPU, an FPGA based accelerator was superior in power performance efficiency. Our method is suitable for the frame object detector for an embedded vision system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FPL.2018.00088 | 2018 28th International Conference on Field Programmable Logic and Applications (FPL) |
Keywords | Field | DocType |
Object Detection,Deep Learning,Embedded System | Object detection,Machine vision,Computer science,16-bit,Field-programmable gate array,Feature extraction,Real-time computing,Frame rate,MPSoC,Detector | Conference |
ISSN | ISBN | Citations |
1946-147X | 978-1-5386-8518-1 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Nakahara | 1 | 155 | 37.34 |
| Masayuki Shimoda | 2 | 8 | 6.45 |
| Shimpei Sato | 3 | 43 | 13.03 |