Improved drone image small target detection based on YOLOv7

Abstract

With the development of artificial intelligence, the use of drones in everyday life is becoming more and more popular, but there is still room for improvement in terms of detection accuracy and detection speed when using drones to capture aerial images for small target detection. This paper proposes a target detection and recognition method based on an improved YOLOv7 for small target detection applied to UAV aerial photography. First, the backbone network of MobileOne is improved, and the MobileOne module is added to the YOLOv7 model, which replaces some of the standard convolutional layers in the YOLOv7 backbone network, effectively speeding up the inference speed and reducing the number of model parameters; secondly, based on the ConvNeXt structure, the CNeB module is constructed to improve the feature extraction capability of the YOLOv7 network and target detection; finally, the Wise-IoU loss function is introduced to reduce the competitiveness of high-quality anchor frames while minimizing the harmful gradients generated by low-quality examples. Our improved method is compared with traditional target detection algorithms such as YOLOv7 on the VisDrone2019 dataset. The results show that the improved method has better detection results.