Optimizing ship detection efficiency in SAR images
- URL: http://arxiv.org/abs/2212.05843v1
- Date: Mon, 12 Dec 2022 12:04:10 GMT
- Title: Optimizing ship detection efficiency in SAR images
- Authors: Arthur Van Meerbeeck, Jordy Van Landeghem, Ruben Cartuyvels,
Marie-Francine Moens
- Abstract summary: The speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing.
We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images.
We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.
- Score: 12.829941550630776
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The detection and prevention of illegal fishing is critical to maintaining a
healthy and functional ecosystem. Recent research on ship detection in
satellite imagery has focused exclusively on performance improvements,
disregarding detection efficiency. However, the speed and compute cost of
vessel detection are essential for a timely intervention to prevent illegal
fishing. Therefore, we investigated optimization methods that lower detection
time and cost with minimal performance loss. We trained an object detection
model based on a convolutional neural network (CNN) using a dataset of
satellite images. Then, we designed two efficiency optimizations that can be
applied to the base CNN or any other base model. The optimizations consist of a
fast, cheap classification model and a statistical algorithm. The integration
of the optimizations with the object detection model leads to a trade-off
between speed and performance. We studied the trade-off using metrics that give
different weight to execution time and performance. We show that by using a
classification model the average precision of the detection model can be
approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.
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