智能制造技术与系统研究中心知识库

In this paper, a real-time small immobile object recognition system is implemented using wavelet moment-based back-propagation(BP) neural network classifier. The system is composed of a camera and an image acquiring and processing board developed by our research team. An FPGA chip and a DSP chip are embedded in the image board as the major calculation units, which make real-time computation possible. First, wavelet moment invariants of training samples are integrated with BP neural network to construct the classifier on the host computer. Then, real-time object detection and classification experiments are conducted according to the classifier on the image acquiring and processing board. Experiment results show that the algorithm can detect and classify different small immobile object types efficiently.