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Video data from the ATR Algorithm Development Image Database is used, containing military and civilian vehicles at different ranges (1000-5000 m). A white-box evasion attack (adversarial objectness gradient) was trained to disrupt a YOLOv3 vehicles detector previously trained on this dataset. Several experiments were performed to assess whether the attack successfully prevented vehicle detection at different ranges. Results show that for an evasion attack trained on object at only 1500 m range and applied to all other ranges, the median mAP reduction is >95%. Similarly, when trained only on two vehicles and applied on all seven remaining vehicles, the median mAP reduction is >95%.
This means that evasion attacks can succeed with limited training data for multiple ranges and vehicles. Although a (perfect-knowledge) white-box evasion attack is a worst-case scenario in which a system is fully compromised, and its inner workings are known to an adversary, this work may serve as a basis for research into robustness and designing AIbased object detectors resilient to these attacks.
In this study, we develop an object detector for 15 vehicle classes, containing similar appearing types, such as multiple battle tanks and howitzers. We show that combining few real data samples with a large amount of simulated data (12,000 images) leads to a significant improvement in comparison with using one of these sources individually. Adding just two samples per class improves the mAP to 55.9 [±2.6], compared to 33.8 [±0.7] when only simulated data is used. Further improvements are achieved by adding more real samples and using Grounding DINO, a foundation model pretrained on vast amounts of data (mAP = 90.1 [±0.5]). In addition, we investigate the effect of simulation variation, which we find is important even when more real samples are available.
Of all available snapshots, only the best and most representative snapshots should be selected for the operator. In this paper, we present two different approaches for snapshot selection from a vessel track. The first is based on directional track information, and the second on the snapshot appearance. We present results for both these methods on IR recordings, containing vessels with different track patterns in a harbor scenario.
In our current research, we propose a ‘maritime detection framework 2.0’, in which multi-platform sensors are combined with detection algorithms. In this paper, we present a comparison of detection algorithms for EO sensors within our developed framework and quantify the performance of this framework on representative data.
Automatic detection can be performed within the proposed framework in three ways: 1) using existing detectors, such as detectors based on movement or local intensities; 2) using a newly developed detector based on saliency on the scene level; and 3) using a state-of-the-art deep learning method. After detection, false alarms are suppressed using consecutive tracking approaches. The performance of these detection methods is compared by evaluating the detection probability versus the false alarm rate for realistic multi-sensor data.
New types of maritime targets require new target detection strategies. Combining new detection strategies with existing tracking technologies shows potential increase in detection performance of the complete framework.
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