Document Type

Conference Proceeding

Publication Date

Spring 4-2026

Abstract

Detecting drones in video streaming environments remains challenging in computer vision due to scale variation and background complexity. Building up from prior work in real-time detection and skeletonization for streaming environments, this study aims to improve small object detection through Skeletonization and a Small-Object-Aware Detection Transformer framework, which uses DETR technology as a foundational step toward reliable motion prediction in dynamic aerial scenes. A transformer-based detection model was trained on a drone dataset converted to COCO format and evaluated using standard COCO metrics, including AP, AP50, and AP_small. Initial testing revealed low-confidence predictions, suggesting limitations in backbone freezing and training configuration. This research focuses on addressing said limitations through retraining the model using full fine-tuning and safer learning rates to enhance feature learning and improve small-object detection performance (AP_small). Preliminary analysis demonstrates the difficulty of small-object detection within transformer-based architectures and highlights the importance of training strategy optimization. This work establishes a foundation for future extensions involving cross-frame object tracking and motion prediction, ultimately contributing to the development of more robust drone monitoring systems. Videos were collected with the help of Rutgers ECE students.

https://www.keanresearchdays.com/2026-student-poster-feed/drones-detection-via-skeletonization-and-small-object-aware-detr

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