A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network

This paper describes a hardware-oriented two-stage algorithm that can be deployed in a resource-limited field-programmable gate array (FPGA) for fast-object detection and recognition with out external memory. The first stage is the bounding boxes proposal with a conventional object detection method, and the second is convolutional neural network (CNN)-based classification for accuracy improvement. Frequently accessing external memories significantly affects the execution efficiency of object classification. Unfortunately, the existing CNN models with a large number of parameters are difficult to deploy in FPGAs with limited on-chip memory resources. In this study, we designed a compact CNN model and performed the hardware-oriented quantization for parameters and intermediate results. As a result, CNN-based ultra-fast-object classification was realized with all parameters and intermediate results stored on chip. Several evaluations were performed to demonstrate the performance of the proposed algorithm. The object classification module consumes only 163.67 Kbits of on-chip memories for ten regions of interest (ROIs), this is suitable for low-end FPGA devices. In the aspect of accuracy, our method provides a correctness rate of 98.01% in open-source data set MNIST and over 96.5% in other three self-built data sets, which is distinctly better than conventional ultra-high-speed object detection algorithms.