Towards Airborne Object Detection: A Deep Learning Analysis

[2601.11907] Towards Airborne Object Detection: A Deep Learning Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2601.11907 (cs) [Submitted on 17 Jan 2026] Title: Towards Airborne Object Detection: A Deep Learning Analysis Authors: Prosenjit Chatterjee , ANK Zaman View a PDF of the paper titled Towards Airborne Object Detection: A Deep Learning Analysis, by Prosenjit Chatterjee and 1 other authors View PDF HTML (experimental) Abstract: The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited scalability and operational inefficiencies. This work introduces a dual-task model based on EfficientNetB4 capable of performing airborne object classification and threat-level prediction simultaneously. To address the scarcity of clean, balanced training data, we constructed the AODTA Dataset by aggregating and refining multiple public sources. We benchmarked our approach on both the AVD Dataset and the newly developed AODTA Dataset and further compared performance against a ResNet-50 baseline, which consistently underperformed EfficientNetB4. Our EfficientNetB4 model achieved 96% accuracy in object classification and 90% accuracy in threat-level prediction, underscoring its promise for applications in surveillance, defense, and airspace management. Although the title references detection, this study focuses specifically on classification and threat-level inference using pre-localized airborne object images provided by existing datasets. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) Cite as: arXiv:2601.11907 [cs.CV] (or arXiv:2601.11907v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2601.11907 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/ISCMI67495.2025.11358609 Focus to learn more DOI(s) linking to related resources Submission history From: A N K Zaman Dr [ view email ] [v1] Sat, 17 Jan 2026 04:47:47 UTC (1,590 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards Airborne Object Detection: A Deep Learning Analysis, by Prosenjit Chatterjee and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-01 Change to browse by: cs cs.AI cs.LG cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy#267675
• Multimodal Solutions for the Detection of Safety-Related Railroad Objects under Adverse Weather: Architectures, Proof-of-Concepts, and Performance Results#977321
• Cloud microphysics training and aerosol inference with the Fiats deep learning library#634684
• RADIX: a deep learning framework that maps root barriers across species and reveals genetic and environmental contributions#668751
• Cost-effective and scalable urban air quality monitoring using image-based deep learning.#242289