| dc.description.abstract | Autonomous ships have the potential to redefine the maritime industry, providing substantial
improvements in safety, economics and fuel efficiency, while creating new previously
unimagined services. Radar is a key part of the solution to the navigation and
collision avoidance challenge for autonomous vessels, due to its high range and accuracy,
as well as robustness against harsh weather conditions. A semantic segmentation
simplifies the radar data into homogeneous clusters of defined targets of interest, allowing
the autonomous agent to understand its environment at a resolution suitable
for high-level reasoning and planning.
A large database of maritime radar envelope data was gathered from two S-band
radar systems along the Norwegian coastline, and ground truth labels were constructed
from Automatic Identification System (AIS) data and chart data. A Machine Learning
(ML) detector, in the form of a Convolutional Neural Network (CNN), was trained in a
supervised manner to segment the radar envelope image into four classes: background
(sea), vessels, land and unknown (i.e. regions that the radar s signals can not reach).
Several modifications to CNN architectures and augmentations to radar envelope data
are suggested and evaluated, with downsampling of the envelope data increasing the
receptive field of the CNN units and replacing batch normalization layers with group
normalization layers, alleviating the adverse effects of limited memory, having the most
significant contributions.
The ML detector exhibits great results on the test set: correctly classifying over 99%
of the navigable area, an overall 69.11 mean Intersection Over Union (IoU) score and
a mean of 75.55 on the Boundary Jaccard (BJ) index. The vessel detection capability is
compared to a traditional approach to radar detection in the form of a Constant False
Alarm Rate (CFAR) detector supplied by Kongsberg Seatex. The ML detector achieves a
higher probability of detection for all false alarm rates, with both detectors managing
a 0.9 probability of detection for a false alarm rate around 2 · 10−2. Below this point,
the ML detector outperforms the CFAR detector by several orders of magnitude on
the false alarm rate. The ML detector also exhibits superior streak performance; the
average and maximum period that vessels go undetected is significantly shorter, especially
at lower false alarm rates. This performance discrepancy can in part be explained
by lower-than-expected performance from a CFAR detector, owing to a difficult, multimodal
data distribution and issues with the vessel labels.
The master project and all experiments are conducted on regular consumer hardware.
The ML detector processes data at a fraction of the data collection rate of radar
systems (0.3 vs. 2.4 seconds). The developed detector system could therefore be deployed
as is, but more research on the effects of adversarial techniques should be conducted
before it is employed for safety-critical tasks. | en |
