Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications
Ultra-small object detection in UAV imagery presents significant challenges due to scale variation, environmental complexity, and computational constraints. This study introduces a quantum-inspired multi-scale object detection model designed to address these issues effectively. By integrating quantu...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816639/ |
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| author | Muhammad Muzammul Muhammad Assam Ayman Qahmash |
| author_facet | Muhammad Muzammul Muhammad Assam Ayman Qahmash |
| author_sort | Muhammad Muzammul |
| collection | DOAJ |
| description | Ultra-small object detection in UAV imagery presents significant challenges due to scale variation, environmental complexity, and computational constraints. This study introduces a quantum-inspired multi-scale object detection model designed to address these issues effectively. By integrating quantum-inspired sub-pixel convolution, adversarial training, and self-supervised learning, the model enhances detection accuracy, robustness, and computational efficiency. These advancements are particularly critical for UAV applications such as surveillance, precision agriculture, disaster response, and environmental monitoring. The proposed model was evaluated on the VisDrone2019 dataset and benchmarked against state-of-the-art methods, including YOLOv4, YOLO11, RT-DETR, and EfficientDet. It achieved 65.3% precision, 52.4% recall, and a mean Average Precision (mAP) of 34.5%, outperforming conventional models in detecting ultra-small objects. Efficiency optimizations, including structured pruning and quantization, reduced computational load to 30 GFLOPS with an inference time of 8.1 milliseconds, ensuring suitability for real-time UAV applications on resource-constrained platforms. This research offers a practical and robust solution for UAV-based object detection tasks, combining state-of-the-art accuracy with operational efficiency. It also establishes a foundation for future advancements, including scalability to diverse datasets, integration with edge computing platforms, and the exploration of quantum computing techniques. These contributions pave the way for enhanced capabilities in computer vision and autonomous aerial systems. |
| format | Article |
| id | doaj-art-38314559b82c485a952c4ef127e29ea3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-38314559b82c485a952c4ef127e29ea32025-01-07T00:01:39ZengIEEEIEEE Access2169-35362025-01-01132173218610.1109/ACCESS.2024.352340510816639Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time ApplicationsMuhammad Muzammul0https://orcid.org/0000-0002-9859-6054Muhammad Assam1https://orcid.org/0000-0001-7331-5351Ayman Qahmash2https://orcid.org/0000-0003-2558-9475College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaInformatics and Computer Systems Department, King Khalid University, Abha, Saudi ArabiaUltra-small object detection in UAV imagery presents significant challenges due to scale variation, environmental complexity, and computational constraints. This study introduces a quantum-inspired multi-scale object detection model designed to address these issues effectively. By integrating quantum-inspired sub-pixel convolution, adversarial training, and self-supervised learning, the model enhances detection accuracy, robustness, and computational efficiency. These advancements are particularly critical for UAV applications such as surveillance, precision agriculture, disaster response, and environmental monitoring. The proposed model was evaluated on the VisDrone2019 dataset and benchmarked against state-of-the-art methods, including YOLOv4, YOLO11, RT-DETR, and EfficientDet. It achieved 65.3% precision, 52.4% recall, and a mean Average Precision (mAP) of 34.5%, outperforming conventional models in detecting ultra-small objects. Efficiency optimizations, including structured pruning and quantization, reduced computational load to 30 GFLOPS with an inference time of 8.1 milliseconds, ensuring suitability for real-time UAV applications on resource-constrained platforms. This research offers a practical and robust solution for UAV-based object detection tasks, combining state-of-the-art accuracy with operational efficiency. It also establishes a foundation for future advancements, including scalability to diverse datasets, integration with edge computing platforms, and the exploration of quantum computing techniques. These contributions pave the way for enhanced capabilities in computer vision and autonomous aerial systems.https://ieeexplore.ieee.org/document/10816639/Ultra-small object detectionUAV imageryquantum-inspired feature pyramidsadversarial trainingself-supervised learningreal-time applications |
| spellingShingle | Muhammad Muzammul Muhammad Assam Ayman Qahmash Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications IEEE Access Ultra-small object detection UAV imagery quantum-inspired feature pyramids adversarial training self-supervised learning real-time applications |
| title | Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications |
| title_full | Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications |
| title_fullStr | Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications |
| title_full_unstemmed | Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications |
| title_short | Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications |
| title_sort | quantum inspired multi scale object detection in uav imagery advancing ultra small object accuracy and efficiency for real time applications |
| topic | Ultra-small object detection UAV imagery quantum-inspired feature pyramids adversarial training self-supervised learning real-time applications |
| url | https://ieeexplore.ieee.org/document/10816639/ |
| work_keys_str_mv | AT muhammadmuzammul quantuminspiredmultiscaleobjectdetectioninuavimageryadvancingultrasmallobjectaccuracyandefficiencyforrealtimeapplications AT muhammadassam quantuminspiredmultiscaleobjectdetectioninuavimageryadvancingultrasmallobjectaccuracyandefficiencyforrealtimeapplications AT aymanqahmash quantuminspiredmultiscaleobjectdetectioninuavimageryadvancingultrasmallobjectaccuracyandefficiencyforrealtimeapplications |