Abstract
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.
Highlights
In recent years, with the rapid development of information technology, intelligent transportation systems have become an important way of modern traffic management and an inevitable trend
A vehicle detection method for aerial image based on YOLO deep learning algorithm is presented
Based on YOLO deep learning algorithm and three public aerial image datasets, this paper presents a vehicle detection method for aerial image
Summary
We process and integrate the above three public aerial image datasets first and modify the network parameters of YOLO algorithm map propriately to train a model.
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