Object Detection Based on Efficient Multiscale Auto-Inference in Remote Sensing Images

Object detection in remote sensing images has important applications in various aspects. Object detection algorithms with deep convolutional neural networks (DCNNs) have made remarkable progress. However, when processing objects on vastly multiple scales in high-resolution optical remote sensing images, there is a high computational cost. Therefore, to simplify neural network multiscale training and inference, an automatic multiscale inference framework is proposed to balance the speed and accuracy of object detection. We use an attention mechanism that uses a key-point network to predict regions with small objects on a coarse scale and only process regions obtained from the first stage on finer scales instead of processing an entire larger scale image. The fully convolutional neural network (CNN) that is used in training and detecting is not affected by the image input resolution. The experiments are carried out using the NWPUVHR-10 data set, and the experimental results show that these methods can improve the training efficiency and detection accuracy in remote sensing images.