Name
Abstract | ||
|---|---|---|
In surveillance cameras, pedestrians and objects are detected using Convolutional Neural Network (CNN) based Object Detection such as YOLO and SSD. Since the size of the CNN input image is fixed, small objects cannot be detected when the high-resolution image is resized. By splitting image and applying object detection to each split image, CNN can detect small objects and take advantage of the high performance of the FPGA. We demonstrate an object detector by splitting the given image as stream data to YOLOv2 on an FPGA which has a very high performance. It achieved both practical speed and accuracy. Due to the difference in scale between training data and test data, the detection of small objects fails when the granularity of split is small. Splitting the image matches the scale of the data. When detection throughput is high enough, it detects many objects with a practical speed. We demonstrate the comparison of FPGA and mobile GPU in the proposed image split method of object detection. We implement Object Detection Systems by Xilinx ZCU104 FPGA board and NVIDIA Mobile GPU boards with a USB camera. The image split method can be adapted as it is to all implementations of object detection. We compare the performance of the FPGA and GPU. As a result of the experiment, FPGA achieves 547.0 FPS per image and is 3.9 times faster than Mobile GPU. When the given image is split into 4 × 4 grids, the system realizes 34.2 FPS and it satisfies the real-time requirement on the standard camera(30FPS). We showed that ultra-fast object detection can be used to improve accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FPL.2019.00078 | 2019 29th International Conference on Field Programmable Logic and Applications (FPL) |
Keywords | Field | DocType |
FPGA, CNN, YOLO | Object detection,Convolutional neural network,Computer science,Field-programmable gate array,Real-time computing,Test data,Throughput,Detector,Pedestrian detection,USB | Conference |
ISSN | ISBN | Citations |
1946-147X | 978-1-7281-4885-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Jinguji | 1 | 5 | 4.18 |
| Youki Sada | 2 | 1 | 1.79 |
| Hiroki Nakahara | 3 | 155 | 37.34 |