ORIGINAL RESEARCH article

Front. Neurorobot.

Volume 19 - 2025 | doi: 10.3389/fnbot.2025.1643011

This article is part of the Research TopicAdvancing Neural Network-Based Intelligent Algorithms in Robotics: Challenges, Solutions, and Future Perspectives - Volume IIView all 4 articles

Integrated Neural Network Framework for Multi-Object Detection and Recognition Using UAV Imagery

Provisionally accepted
Mohammed  AlshehriMohammed Alshehri1Tingting  XueTingting Xue2Ghulam  MujtabaGhulam Mujtaba3Yahya  AlqahtaniYahya Alqahtani1Nouf  Abdullah AlmujallyNouf Abdullah Almujally4Ahmad  JalalAhmad Jalal3*Hui  LiuHui Liu5*
  • 1King Khalid University, Abha, Saudi Arabia
  • 2Nanjing University of Information Science and Technology, Nanjing, China
  • 3Air University, Islamabad, Pakistan
  • 4Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 5Universitat Bremen, Bremen, Germany

The final, formatted version of the article will be published soon.

The framework suggests using today's best neural networks that are made to solve different problems in aerial vehicle analysis. RetinexNet is used in preprocessing to make the lighting of each input frame consistent. Using HRNet for semantic segmentation allows for accurate splitting between vehicles and their surroundings. YOLOv11 provides high precision and quick vehicle detection and Deep SORT allows reliable tracking without losing track of individual cars. CSRNet are used for vehicle counting that is unaffected by obstacles or traffic jams. LSTM models capture how a car moves in time to forecast future positions. Combining DenseNet and SuperPoint embeddings that were improved with an AutoEncoder is done during feature extraction. In the end, using an attention function, Vision Transformer-based models classify vehicles seen from above. Every part of the system is developed and included to give the improved performance when the UAV is being used in real life.Results: Our proposed framework significantly improves the accuracy, reliability, and efficiency of vehicle analysis from UAV imagery. Our pipeline was rigorously evaluated on two famous datasets, AU-AIR and Roundabout. On the AU-AIR dataset, the system achieved a detection accuracy of 97.8%, a tracking accuracy of 96.5%, and a classification accuracy of 98.4%. Similarly, on the Roundabout dataset, it reached 96.9% detection accuracy, 94.4% tracking accuracy, and 97.7% classification accuracy. These results surpass previous benchmarks, demonstrating the system's robust performance across diverse aerial traffic scenarios. The integration of advanced models, YOLOv11 for detection, HRNet for segmentation, Deep SORT for tracking, CSRNet for counting, LSTM for trajectory prediction, and Vision Transformers for classification enables the framework to maintain high accuracy even under challenging conditions like occlusion, variable lighting, and scale variations.The outcomes show that the chosen deep learning system is powerful enough to deal with the challenges of aerial vehicle analysis and gives reliable and precise results in all the aforementioned tasks. Combining several advanced models ensures that the system works smoothly even when dealing with problems like people being covered up and varying sizes.

Keywords: Unmanned Aerial Vehicle, neural network models, deep learning, Multi-object recognition, Transfer Learning, Intelligent detector, Autonomous system Unmanned aerial vehicle, autonomous system

Received: 07 Jun 2025; Accepted: 02 Jul 2025.

Copyright: © 2025 Alshehri, Xue, Mujtaba, Alqahtani, Almujally, Jalal and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Ahmad Jalal, Air University, Islamabad, Pakistan
Hui Liu, Universitat Bremen, Bremen, Germany

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