A Vehicle Detection Method for Aerial Image Based on YOLO

With the application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become a key engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial image based on YOLO deep learning algorithm is presented. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements.

[1]  Moshe Ben-Akiva,et al.  Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing , 2014 .

[2]  Mohan M. Trivedi,et al.  A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking , 2010, IEEE Transactions on Intelligent Transportation Systems.

[3]  Guoqing Zhou,et al.  DETECTING AND COUNTING VEHICLES FROM SMALL LOW-COST UAV IMAGES , 2009 .

[4]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Venkatesan Muthukumar,et al.  Video Based Vehicle Detection and Its Application in Intelligent Transportation Systems , 2012 .

[6]  Frédéric Jurie,et al.  Vehicle detection in aerial imagery : A small target detection benchmark , 2016, J. Vis. Commun. Image Represent..

[7]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jiebo Luo,et al.  DOTA: A Large-Scale Dataset for Object Detection in Aerial Images , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Akihiro Takeuchi,et al.  On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[11]  Wesam A. Sakla,et al.  A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning , 2016, ECCV.