Electric cars, as an alternative to fuel cars, are growing rapidly in number and help reduce carbon dioxide emissions and build green cities. However, due to the appearance similarities between electric and fuel cars, it is still a challenge to count the proportion of electric cars on the road. Considering that thermal infrared (TIR) imaging technology can obtain the temperature characteristics of cars as well as work at night, this paper proposes a new solution to identify electric and fuel cars in Unmanned aerial vehicle (UAV)-based TIR images. First, a semiautomated labelling method is implemented for training data set generation, which is based on a traditional object tracking algorithm to improve labelling efficiency. Then, two classic deep convolutional neural networks, YOLOv5 and SSD, are used to verify the reliability of the dataset. Experiments prove that the proposed solution can effectively identify electric and fuel cars on the road, with mean of average precision (mAP) up to 0.99. This study is the first attempt to apply UAV-based TIR imaging to electric and fuel car identification and propose the first open data set for relevant research.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)