The wide range, complex background, and small target size of aerial remote sensing images results in the low detection accuracy of remote sensing target detection algorithms. Traditional detection algorithms have low accuracy and slow speed, making it difficult to achieve the precise positioning of small targets. This paper proposes an improved algorithm based on You Only Look Once (YOLO)-v3 for target detection of remote sensing images. Due to the difficulty in obtaining the datasets, research on small targets for complex images, such as airplanes and ships, is the focus of research. To make up for the problem of insufficient data, we screen specific types of training samples from the DOTA (Dataset of Object Detection in Aerial Images) dataset and select small targets in two different complex backgrounds of airplanes and ships to jointly evaluate the optimization degree of the improved network. We compare the improved algorithm with other state-of-the-art target detection algorithms. The results show that the performance indexes of both datasets are ameliorated by 1–3%, effectively verifying the superiority of the improved algorithm.
Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network
