Citation

Liu Y, Wang J, Qiu T, Qi W. Entropy (Basel) 2021; 23(10): e1358.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e23101358

PMID

34682082

PMCID

PMC8535027

Abstract

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

Language: en

Keywords

entropy; resistance to shake; unmanned aerial vehicle; vehicle detection