The Design of Objects Bounding Boxes Non-Maximum Suppression and Visualization Module Based on FPGA

Abstract

The objects detection results are marked with bounding boxes in image. There are lots of redundant bounding boxes in detection results. Generally, the non-maximum suppression (NMS) method is used to remove redundant bounding boxes in detection results to improve accuracy of detection. Opencv is usually used to complete visualization of objects bounding boxes. We compare time of NMS and bounding boxes visualization implemented in FPGA and software in server. The results in hardware performs better. Objects detection has high requirements for real-time performance. Parallel computing capabilities of hardware makes it better than software to implement object detection algorithm and related peripheral algorithms in FPGA. FPGA is a good solution for embedded applications of objects detection. In this paper, we build a FPGA based objects detection system on YOLO, which is a kind of object detection algorithm based on deep learning and we complete the design and verification of back-end processing module in the system, including NMS module to remove redundant objects bounding boxes and bounding boxes visualization module. We make some changes for the NMS algorithm implementation in hardware. Two modules work together to process 100x61 images including 98x20 object bounding boxes. The remaining bounding boxes what we want after removing redundant bounding boxes are used for display, which ultimately takes 680μs.