Adaptive Anchor for Fast Object Detection in Aerial Image

Object detection in aerial images is an important task for many applications such as reconnaissance, surveillance, search, and rescue. At present, convolution neural network-based aerial image object detection algorithms mainly focus on rotation invariance and scale invariance, but ignore an important characteristic of the aerial image that the image captured height is an important prior knowledge. At the same captured height, the target has a clear scale range. In this letter, a scale-aware network is proposed to determine the scale of predefined anchors, which can effectively reduce the scale search range, reduce the risk of overfitting, and improve the detection accuracy and speed in aerial images. Experiments on the VisDrone data set show that the proposed method can not only improve the detection speed by 18% but also improve the average accuracy by 1.6%.