The Application of Deep Learning Algorithms to Longwave Infrared Missile Seekers

Abstract

Convolutional neural networks (CNNs) have already surpassed human-level performance in complex computer vision applications, and can potentially significantly advance the performance of infrared anti-ship guided missile seeker algorithms. But the performance of CNN-based algorithms is very dependent on the data used to optimise them, typically requiring large sets of fully-annotated real-world training examples. Across four technical chapters, this thesis addresses the challenges involved with applying CNNs to longwave infrared ship detection, recognition, and identification. Across four technical chapters, this thesis addresses the challenges involved with applying CNNs to longwave infrared ship detection, recognition, and identification. The absence of suitable longwave infrared training data was addressed through the synthetic generation of a large, thermally-realistic dataset of 972,000 fully labelled images of military ships with varying seascapes and background clutter. This dataset—IRShips—is the largest openly available repository of such images worldwide. Configurable automated workflow pipelines significantly enhance the development of CNN-based algorithms. No such tool was available when this body of work began, so an integrated modular deep learning development environment—Deeplodocus—was created. Publicly-available, it now features among the top 50% of packages on the Python Package Index repository. Using Deeplodocus, the fully-convolutional one-stage YOLOv3 object detection algorithm was trained to detect ships in a highly-cluttered sequence of real world longwave infrared imagery. Further enhancement of YOLOv3 resulted in an F-score of 0.945 being achieved, representing the first time synthetic data has been used to train a CNN algorithm to successfully detect military ships in longwave infrared imagery.Benchmarking YOLOv3’s detection accuracy against two alternative CNNs— Faster R-CNN and Mask R-CNN—using visual-spectrum and near-infrared data from the Singapore Maritime dataset, showed that YOLOv3 was three times faster, but 3% less accurate than Mask R-CNN. Modifying YOLOv3 through the use of spectral domain-dependent encoding delivered state-of-the-art accuracy with respect to the near-infrared test data, while maintaining YOLOv3’s considerable speed advantage.