Lightweight Deep Network With Context Information and Attention Mechanism for Vehicle Detection in Aerial Image
Jiaquan Shen, Ningzhong Liu, Han Sun, Deguang Li, Yongxin Zhang
Abstract
Vehicle detection in aerial photography scenarios has a wide range of promising applications in the military and civilian fields. Recently, object detection algorithms based on depth models have shown superior performance in aerial vehicle detection tasks. However, these detection algorithms are often accompanied by a large amount of computation and resource consumption, which leads to the inability to perform real-time detection. In addition, the insufficient feature extraction capability of the vehicle and the complex background information also lead to low detection accuracy. In this letter, we propose a lightweight backbone network with a context information module and an attention mechanism module for vehicle detection in the aerial image, which enables the feature extraction network to increase the utilization of contextual information and salient regions. In addition, we use adaptive anchor-free in the detection model to predict the bounding box. The proposed detection algorithm achieves 89.7% and 94.1% mean average precision (mAP) on the German Aerospace Center (DLR)-3K dataset and the created dataset, and the detection time for each image is 1.66 and 0.049 s, respectively.