Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more adopted to capture the aerial images. Therefore, how to fully utilize the aerial infrared images for the vehicle detection has attracted widespread attentions. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is really difficult to implement the vehicle detection in the aerial infrared images. In our work, an efficient and accurate vehicle detection algorithm in aerial infrared images is proposed via an improved yolov3 network. To increase the detection efficiency, we construct a new structure of the improved yolov3 network with only 16 layers. Besides, we expand the anchor boxes to four scales to improve the detection accuracy of the small vehicles. Meanwhile, for the limitation of the infrared vehicle samples, the transfer learning is introduced to train the improved yolov3 network. Finally, the proposed algorithm is evaluated on the VIVID and NPU data sets. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive vehicle detection results.