A case study on FPGA implementation of parts counting orientation recognition method for industrial vision system

Abstract

This paper presents a case study of FPGA implementation for electrical parts counting and orientation recognition method based on industrial vision system. Since parts counting and orientation recognition can be achieved using a trained neural network (NN), the paper studied its efficient implementation using FPGA. Contributions include weights binarization, activation function approximation and HW architecture design for NN recognition. Experimental results revealed that the proposed implementation achieved faster speed and lower hardware resource usage.