Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial intelligence (AI) significantly boosted the potential performance of ATD/R. However, the scarcity of publicly available military datasets limits the application of these systems. As a solution, this paper explores the use of publicly available models and civilian datasets to achieve reasonable performance in military contexts. We benchmark several state-of-the-art models, including six iterations of the YOLO series and
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 49%Deep Reinforcement Learning-based combat recognition of traditional Chinese Sanda under artificial intelligence technology - Nature →
- LinkedLinked via arxiv author · 85%Alma M. Liezenga →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Lotte Nijskens →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Henrik R. Baumann →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Stefan Becker →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Simon Bensberg →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Niccolò Camarlinghi →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”
- LinkedLinked via arxiv author · 85%Håvard R. Eiring →
“Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition”