Vehicle Detection in High-Resolution Images Using Superpixel Segmentation and CNN Iteration Strategy

Abstract

This letter presents a study of vehicle detection in high-resolution images using superpixel segmentation and iterative convolutional neural network strategy. First, a novel superpixel segmentation integrated with multiple local information constraints method is proposed to improve the segmentation results with a low breakage rate. To make training and detection more efficient, we extract meaningful and nonredundant patches based on the centers of the segmented superpixels. For reducing the instability in detection performance because of manual or random selection of samples, a training sample iterative selection strategy based on convolutional neural network is proposed. After a compact training sample subset is obtained from the original entire training set, a representative feature set with high discrimination ability between vehicle and background is extracted from these selected samples for detection. To further avoid overfitting the training and promote the detection efficiency, data augment and a main direction estimation method are used. Comparative experimental results on Toronto data indicated the effectiveness of our proposed method.