Titre :	A lightweight multi-task deep learning framework for uav detection and tracking
Type de document :	document multimédia
Auteurs :	Amina Safaa Ben Messaoud, Auteur ; Nardjes Hamini, Directeur de thèse
Editeur :	Laghouat : Université Amar Telidji - Département d'informatique
Année de publication :	2025
Importance :	49 p.
Accompagnement :	1 disque optique numérique (CD-ROM)
Note générale :	Option : Data science et artificial intelligence
Langues :	Anglais (eng)
Mots-clés :	UAVs  MTL  YOLO  Faster-RCNN-Resnet50  Tracking
Résumé :	In this work, we propose a custom Multi-Task Learning (MTL) model for real-time UAV detection and tracking, designed to jointly perform classification and bounding box regression. The proposed model was evaluated against state-of-the-art detectors, including YOLOv8 and Faster R-CNN ResNet-50. While YOLOv8 achieved fast inference with strong accuracy, and Faster R-CNN demonstrated high precision in complex scenes, our MTL model outperformed both in classification accuracy and bounding box precision. Specifically, the MTL model achieved a classification accuracy of 98.53%, a bounding box MAE of 0.0256, and an MSE of 0.0027, demonstrating its effectiveness in multi-output learning. To enable tracking, we integrated a Kalman Filter, which maintained consistent ob- ject identities across frames . These results highlight the robustness and efficiency of the proposed MTL-based pipeline for UAV detection and tracking in real-time surveillance applications.
note de thèses :	Mémoire de master en informatique

A lightweight multi-task deep learning framework for uav detection and tracking [document multimédia] / Amina Safaa Ben Messaoud, Auteur ; Nardjes Hamini, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 49 p. + 1 disque optique numérique (CD-ROM).
Option : Data science et artificial intelligence
Langues : Anglais (eng)

Mots-clés :	UAVs  MTL  YOLO  Faster-RCNN-Resnet50  Tracking
Résumé :	In this work, we propose a custom Multi-Task Learning (MTL) model for real-time UAV detection and tracking, designed to jointly perform classification and bounding box regression. The proposed model was evaluated against state-of-the-art detectors, including YOLOv8 and Faster R-CNN ResNet-50. While YOLOv8 achieved fast inference with strong accuracy, and Faster R-CNN demonstrated high precision in complex scenes, our MTL model outperformed both in classification accuracy and bounding box precision. Specifically, the MTL model achieved a classification accuracy of 98.53%, a bounding box MAE of 0.0256, and an MSE of 0.0027, demonstrating its effectiveness in multi-output learning. To enable tracking, we integrated a Kalman Filter, which maintained consistent ob- ject identities across frames . These results highlight the robustness and efficiency of the proposed MTL-based pipeline for UAV detection and tracking in real-time surveillance applications.
note de thèses :	Mémoire de master en informatique