36,519 research outputs found
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition
Convolutional Neural Networks (CNN) have revolutionized perception for color
images, and their application to sonar images has also obtained good results.
But in general CNNs are difficult to train without a large dataset, need manual
tuning of a considerable number of hyperparameters, and require many careful
decisions by a designer. In this work, we evaluate three common decisions that
need to be made by a CNN designer, namely the performance of transfer learning,
the effect of object/image size and the relation between training set size. We
evaluate three CNN models, namely one based on LeNet, and two based on the Fire
module from SqueezeNet. Our findings are: Transfer learning with an SVM works
very well, even when the train and transfer sets have no classes in common, and
high classification performance can be obtained even when the target dataset is
small. The ADAM optimizer combined with Batch Normalization can make a high
accuracy CNN classifier, even with small image sizes (16 pixels). At least 50
samples per class are required to obtain test accuracy, and using
Dropout with a small dataset helps improve performance, but Batch Normalization
is better when a large dataset is available.Comment: Author version; IEEE/MTS Oceans 2017 Aberdee
Improving Sonar Image Patch Matching via Deep Learning
Matching sonar images with high accuracy has been a problem for a long time,
as sonar images are inherently hard to model due to reflections, noise and
viewpoint dependence. Autonomous Underwater Vehicles require good sonar image
matching capabilities for tasks such as tracking, simultaneous localization and
mapping (SLAM) and some cases of object detection/recognition. We propose the
use of Convolutional Neural Networks (CNN) to learn a matching function that
can be trained from labeled sonar data, after pre-processing to generate
matching and non-matching pairs. In a dataset of 39K training pairs, we obtain
0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary
classification matching decision, and 0.89 AUC for another CNN that outputs a
matching score. In comparison, classical keypoint matching methods like SIFT,
SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods
obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and
a Support Vector Machine resulting in AUC 0.66.Comment: Author versio
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