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The aim is to enhance object detection and tracking in aerial surveillance images using advanced deep learning techniques.

An Automated Object Detection and Tracking System in Surveillance Aerial Images Using 3D Adaptive Multi‐Dilated Yolov8 With Attention Mechanism

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The aim is to enhance object detection and tracking in aerial surveillance images using advanced deep learning techniques.
Utilized the 3D Adaptive Multi-Dilated Yolov8 with Attention Mechanism for detection and tracking.
Employed the Improved Lyrebird Optimization Algorithm for optimizing parameters.
Conducted numerical experiments to evaluate performance against existing techniques.
Achieved a localization accuracy of 95.14% with the proposed method.
Showed significant improvements over existing methods such as MTBO-3DAMDY-AM and CNN with better rates in mean Average Precision.
Performance metrics indicated enhancements across various statistical benchmarks compared to outdated techniques.

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Automated system detects and tracks objects in aerial images, suggesting improvements for surveillance applications.

Shravya et al. (2026) studied this question.

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