Journal article
Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images
Automation and Control, Department of Electrical Engineering, Technical University of Denmark1
Department of Electrical Engineering, Technical University of Denmark2
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
This paper assesses the performance of three convolutional neural networks for object detection at sea using Long Wavelength Infrared (LWIR) images in the 8 − 14µm range. Capturing images from ferries and annotating 20k images, fine-tuning is done of three state of art deep neural networks: RetinaNet, YOLO and Faster R-CNN.
Targeting on vessels and buoys as two main classes of interest for navigation, performance is quantified by the cardinality of true and false positives and negatives in a random validation set. Calculating precision and recall as functions of tuning parameters for the three classifiers, noticeable differences are found between the three networks when used for LWIR image object classification at sea.
The results lead to conclusions on imaging requirements when classification is used to support navigation.
Language: | English |
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Year: | 2019 |
Pages: | 64-71 |
Proceedings: | 12th Control Applications in Marine Systems, Robotics, and Vehicles |
ISSN: | 14746670 and 24058963 |
Types: | Journal article |
DOI: | 10.1016/j.ifacol.2019.12.284 |
ORCIDs: | Stets, Jonathan Dyssel and Blanke, Mogens |