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Smart city article details
| Title | Drone-Based Rgb-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning |
|---|---|
| ID_Doc | 21083 |
| Authors | Sun Y.; Cao B.; Zhu P.; Hu Q. |
| Year | 2022 |
| Published | IEEE Transactions on Circuits and Systems for Video Technology, 32, 10 |
| DOI | http://dx.doi.org/10.1109/TCSVT.2022.3168279 |
| Abstract | Drone-based vehicle detection aims at detecting vehicle locations and categories in aerial images. It empowers smart city traffic management and disaster relief. Researchers have made a great deal of effort in this area and achieved considerable progress. However, because of the paucity of data under extreme conditions, drone-based vehicle detection remains a challenge when objects are difficult to distinguish, particularly in low-light conditions. To fill this gap, we constructed a large-scale drone-based RGB-infrared vehicle detection dataset called DroneVehicle, which contains 28, 439 RGB-infrared image pairs covering urban roads, residential areas, parking lots, and other scenarios from day to night. Cross-modal images provide complementary information for vehicle detection, but also introduce redundant information. To handle this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to improve detection performance in complex environments. Specifically, we design an uncertainty-aware module using cross-modal intersection over union and illumination estimation to quantify the uncertainty of each object. Our method takes uncertainty as a weight to boost model learning more effectively while reducing bias caused by high-uncertainty objects. For more robust cross-modal integration, we further perform illumination-aware non-maximum suppression during inference. Extensive experiments on our DroneVehicle and two challenging RGB-infrared object detection datasets demonstrated the advanced flexibility and superior performance of UA-CMDet over competing methods. Our code and DroneVehicle will be available: https://github.com/VisDrone/DroneVehicle. © 1991-2012 IEEE. |
| Author Keywords | Cross-modality; drone-based vehicle detection; feature fusion; uncertainty-aware |
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