Target Detection Method Based on Improved Particle Search and Convolution Neural Network

Abstract

The border regression is a key technique of the regional convolution neural network (CNN) to locate the target. However, it relies on the border label information of a large number of sample data. Therefore, it is inefficient to generate the training sample set, and the location of the target is also inaccurate. For this, a novel target detection method based on the CNN and the particle search is proposed. A small number of probe particles are generated to roughly locate the target. The CNN is used to extract the image features, determine the target probability, and recognize the pattern of the target. A large number of searching particles are placed near the region where the target features are detected by the probe particles. The nearest neighbor clustering algorithm is used to classify the particles, which are recognized as the same category into different target sets. The positions of the targets can be determined by the bounding rectangle of the searching particles in the same target set. The method can be used to recognize and locate various kinds of targets. Furthermore, the method need not label the borders of the targets in the training samples, which enhance the generation efficiency of the samples. The simulation results show that the correctness of the recognition can be slightly improved, and the accuracy of the location can be significantly improved.