Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via Uncertainty-Aware Learning
By Yiming Sun, Bing Cao, Pengfei Zhu, Qinghua Hu. IEEE transactions on circuits and systems for video technology (Print), 2020
617 citations recorded by the source corpus.
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.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/13b63268296de6f9a40b570f3ca102f23a99e946
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Citations in the corpus
- Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation (2023, methodology): “To generate visual-appealing fused images along with accurate scene segmentation results, we jointly formulate the two tasks into one goal [34, 35, 36], which can be rewritten as: min ω u , ω s f u u , Φ( x , y ; ω u ) + f s s , Ψ( x , y ; ω s ) + g ( ω ⋆ ) , (2) where ω ⋆ = [ ω u , ω s ] . u and s…”