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Suppression of false alarms in Automated Target Recognizers that use Machine Learning

Description:

TECHNOLOGY AREA(S): Info Systems, Human Systems 

OBJECTIVE: Develop methods to suppress false alarms in Automated Target Recognition systems that use machine learning to recognize designated objects. 

DESCRIPTION: Over the past decade, improvements to machine learning techniques have enabled them to eclipse the performance of other computer vision algorithms for object recognition. In numerous competitions, such as the ImageNet Large-Scale Visual Recognition Challenge (1) as well in production software (on Google+, for instance), convolutional neural networks (CNNs) have proven to be a useful method to perform object recognition on large databases (2). These approaches can be used to extract specific vehicle types from broad overhead imagery (3), whether provided by electro-optical (EO) imagery, or synthetic aperture radar (SAR) imagery. Automatic Target Recognition (ATR) plays a critical role in identifying targets and reducing operator workload, however, there are increasing challenges that modern ATR algorithms must address. False alarms have historically presented challenges for ATR algorithms, and systems based on machine learning techniques to date are still subject to excessively high rates of false alarms. A further challenge for SAR ATR is that target types are growing into hierarchies that are more complex (4). Variants to vehicle classes continue growing as militaries modernize older machines, and deploy increasing numbers of variants in increasingly wide varieties of environments. Observational environments vary from open fields to dense urban terrain where clutter and obscurations further affect target recognition capabilities, and the opening of the aperture of environments can expect to result in greater numbers of false alarms when systems use primarily the image data constrained to the local chip that is ingested by the recognition engine. While neural network approaches such as CNNs are easy to implement and have yielded superior performance when trained, there is a strong need to implement methods to reduce false alarm rates that would otherwise require analyst review to dismiss objects or coincidences that are not among the target types. An approach to suppress false alarms might use greater contextual information (5), models of likely deployment, mensuration, fusion and/or verification by reference to other imagery sources (which might include other modalities). Adversarial learning might be used to implement any or all of these approaches, but would need to demonstrate a maintained level of detection without a great deal of manual effort to negate false alarms. 

PHASE I: Implement or collect neural network –trained ATRs, and identify a method and demonstrate the feasibility of suppressing false alarms through a pilot study. 

PHASE II: Implement and evaluate efficient algorithmic processes to suppress false alarms in large image datasets. Test on a larger sample of (potentially classified) government-provided data. Measure performance enhancements in terms of precision and recall, or appropriate metrics that would correlate with workload requirements that would improve analyst efficiency by concentrating their efforts on objects of true intelligence value. 

PHASE III: A computer vision system that is robust to false object detections could significantly impact a broad range of military and civilian applications; examples include car counting, maritime safety of navigation, automatic map updating, and disaster response. 

REFERENCES: 

1: Russakovsky, O., et al. ImageNet Large Scale Visual Recognition Challenge. arXiv 1409.0575v3, 2015

2:  Ren, S., He, K., Girshick, R., Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv 1506.01497v3, 2016

3:  Mundhenk, T., Konjevod, G., Sakla, W., Boakye, K. A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. arXiv 1609.04453, 2016

4:  Zamir, A., Wu, T., Shen, W., Malik, J., Savarese, S. Feedback Networks. arXiv 1612.09508, 2016

5:  Zagoruyko, S., Lerer, A., Lin, T., Pinheiro, P., Gross, S., Chintala, S., Dollar, P. A MultiPath Network for Object Detection. arXiv 1604.02135v2, 2016

KEYWORDS: Automatic Target Recognition, ATR, False Alarms, ROC Curves, Clutter Suppression 

CONTACT(S): 

Samuel Dooley 

(571) 557-7312 

Samuel.W.Dooley@nga.mil 

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