Object Recognition Algorithm Based on an Improved Convolutional Neural Network

Abstract

In order to accomplish the task of object recognition in natural scenes, a new object recognition algorithm based on an improved convolutional neural network (CNN) is proposed. First, candidate object windows are extracted from the original image. Then, candidate object windows are input into the improved CNN model to obtain deep features. Finally, the deep features are input into the Softmax and the confidence scores of classes are obtained. The candidate object window with the highest confidence score is selected as the object recognition result. Based on AlexNet, Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer, which widens the network and deepens the network at the same time. Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images, and has a higher degree of accuracy than the classical algorithms in the field of object recognition.