Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition

Abstract

This paper views target detection and pattern recognition as a kind of communications problem and applies error-correcting coding to the outputs of a convolutional neural network to improve the accuracy and reliability of detection and recognition of targets. The outputs of the convolutional neural network are designed according to codewords with maximum Hamming distances. The effects of the codewords on the performance of the convolutional neural network in target detection and recognition are then investigated. Images of hand-written digits and printed English letters and symbols are used in the experiments. Results show that error-correcting output coding provides the neural network with more reliable decision rules and enables it to perform more accurate and reliable detection and recognition of targets. Moreover, our error-correcting output coding can reduce the number of neurons required, which is highly desirable in efficient implementations.