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MS-CADNet: A Multi-Scale Context Attention Network for Efficient Object Detection in UAV Imagery
1
School of Technology, Cardiff Metropolitan University, Cardiff CF5 2YB, United Kingdom
2
Department of IT and Computer Science, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur 22620, Pakistan
3
Research Unit for Robophilosophy and Integrative Social Robotics (RISR), Aarhus University, Aarhus 8000, Denmark
* Corresponding Author:
Ghulam E Mustafa Abro,
[email protected]
Volume 3, Issue 2
2026
Received: 29 March 2026
Revised: 13 April 2026
Accepted: 19 April 2026
Published: 23 April 2026
Article Information
Abstract
With the rapid advancement of unmanned aerial vehicle (UAV) technology, there is a need for lightweight and accurate object detection on resource-constrained platforms. This paper proposes MS-CADNet, an anchor-free network for small object detection in aerial imagery. It uses a MobileNetV3-Small backbone and a two-branch gated Context Attention Module (CAM) to enhance feature quality. On the VisDrone-DET benchmark, it achieves 31.2% mAP, surpassing YOLOv8-Small and CEASC. The model attains 19.2% AP for small objects with only 3.1M parameters and 5.4 GFLOPs, making it suitable for real-time UAV deployment.
Graphical Abstract
Keywords
UAV
small object detection
context attention module
lightweight neural network
VisDrone dataset
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Bari, A., Memon, F., Waheed, H., & Abro, G. E. M. (2026). MS-CADNet: A Multi-Scale Context Attention Network for Efficient Object Detection in UAV Imagery. ICCK Transactions on Sensing, Communication, and Control, 3(2), 64-75. https://doi.org/10.62762/TSCC.2026.214827
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TY - JOUR AU - Bari, Abdul AU - Memon, Fatima AU - Waheed, Hafsa AU - Abro, Ghulam E Mustafa PY - 2026 DA - 2026/04/23 TI - MS-CADNet: A Multi-Scale Context Attention Network for Efficient Object Detection in UAV Imagery JO - ICCK Transactions on Sensing, Communication, and Control T2 - ICCK Transactions on Sensing, Communication, and Control JF - ICCK Transactions on Sensing, Communication, and Control VL - 3 IS - 2 SP - 64 EP - 75 DO - 10.62762/TSCC.2026.214827 UR - https://www.icck.org/article/abs/TSCC.2026.214827 KW - UAV KW - small object detection KW - context attention module KW - lightweight neural network KW - VisDrone dataset AB - With the rapid advancement of unmanned aerial vehicle (UAV) technology, there is a need for lightweight and accurate object detection on resource-constrained platforms. This paper proposes MS-CADNet, an anchor-free network for small object detection in aerial imagery. It uses a MobileNetV3-Small backbone and a two-branch gated Context Attention Module (CAM) to enhance feature quality. On the VisDrone-DET benchmark, it achieves 31.2% mAP, surpassing YOLOv8-Small and CEASC. The model attains 19.2% AP for small objects with only 3.1M parameters and 5.4 GFLOPs, making it suitable for real-time UAV deployment. SN - 3068-9287 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Bari2026MSCADNet,
author = {Abdul Bari and Fatima Memon and Hafsa Waheed and Ghulam E Mustafa Abro},
title = {MS-CADNet: A Multi-Scale Context Attention Network for Efficient Object Detection in UAV Imagery},
journal = {ICCK Transactions on Sensing, Communication, and Control},
year = {2026},
volume = {3},
number = {2},
pages = {64-75},
doi = {10.62762/TSCC.2026.214827},
url = {https://www.icck.org/article/abs/TSCC.2026.214827},
abstract = {With the rapid advancement of unmanned aerial vehicle (UAV) technology, there is a need for lightweight and accurate object detection on resource-constrained platforms. This paper proposes MS-CADNet, an anchor-free network for small object detection in aerial imagery. It uses a MobileNetV3-Small backbone and a two-branch gated Context Attention Module (CAM) to enhance feature quality. On the VisDrone-DET benchmark, it achieves 31.2\% mAP, surpassing YOLOv8-Small and CEASC. The model attains 19.2\% AP for small objects with only 3.1M parameters and 5.4 GFLOPs, making it suitable for real-time UAV deployment.},
keywords = {UAV, small object detection, context attention module, lightweight neural network, VisDrone dataset},
issn = {3068-9287},
publisher = {Institute of Central Computation and Knowledge}
}
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