Abstract
Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,pre-cise positioning,and rapid transmission.However,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications.Therefore,we propose mask adaptive transformer(MAT)tailored for such scenarios.Specifically,we introduce a structure that supports collabora-tive token sparsification in support windows,enhancing fault tolerance and reducing computational overhead.This structure comprises two modules:a binary mask strategy and adaptive window self-attention(A-WSA).The binary mask strategy focuses on significant objects in various complex scenes.The A-WSA mechanism is employed to self-attend for balance performance and computational cost to select objects and isolate all contextual leakage.Exten-sive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method.Specifically,it achieves a mean average precision(mAP@0.5)improvement of 1.25％over car de-tector based on you only look once version 5(CD-YOLOv5)on the CarPK dataset and a 3.75％average precision(AP@0.5)improvement over cascaded zoom-in detector(CZ Det)on the VisDrone dataset.
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