Problem
Research questions and friction points this paper is trying to address.
Automatic Target Detection and Recognition
military datasets
object detection
Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition
🤖 AI Summary
研究使用公开模型和民用数据集解决军事目标检测与识别问题，通过在新获取的军事相关数据集上测试多种模型，发现大模型及DETR框架表现较好。
📝 Abstract
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 two variations on the DETR framework, on a newly acquired military relevant dataset. This dataset features military vehicles and challenging circumstances, including various degrees of occlusions and small targets. The out-of-the-box version of each model is validated alongside a version finetuned on the VisDrone dataset. This dataset features small objects, an Air-to-Ground (A2G) perspective and relevant classes, potentially generalizing to our military ATD/R task. We compare the performance of the models using mAP@0.5 and mAP@0.5:0.95, across A2G and Ground-to-Ground (G2G) perspective, target size and model size, giving insight into the real-time capabilities of models. Our main findings are: (1) bigger models outperform smaller models, (2) DETR-based models show promising results compared to the YOLO series,(3) fine-tuning models on an out-of-domain A2G dataset, improves their A2G performance and slightly improves their performance on small objects, but (4) all models still struggle with detecting small objects in an A2G scenario. We conclude that, despite recent advances in object detection, in-domain training is still crucial for creating capable ATD/R systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Automatic Target Detection and Recognition
Out-of-the-Box Technology
Publicly Available Models
Civilian Datasets
Fine-Tuning
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