Source Feature Compression for Object Classification in Vision-Based Underwater Robotics

New efficient source feature compression solutions are proposed based on a two-stage Walsh-Hadamard Transform (WHT) for Convolutional Neural Network (CNN)-based object classification in underwater robotics. The object images are firstly transformed by WHT following a two-stage process. The transform-domain tensors have large values concentrated in the upper left corner of the matrices in the RGB channels. By observing this property, the transform-domain matrix is partitioned into inner and outer regions. Consequently, two novel partitioning methods are proposed in this work: (i) fixing the size of inner and outer regions; and (ii) adjusting the size of inner and outer regions adaptively per image. The proposals are evaluated with an underwater object dataset captured from the Raritan River in New Jersey, USA. It is demonstrated and verified that the proposals reduce the training time effectively for learning-based underwater object classification task and increase the accuracy compared with the competing methods. The object classification is an essential part of a vision-based underwater robot that can sense the environment and navigate autonomously. Therefore, the proposed method is well-suited for efficient computer visionbased tasks in underwater robotics applications.

[1]  A. Enis Çetin,et al.  Energy Efficient Hadamard Neural Networks , 2018, ArXiv.

[2]  Dario Pompili,et al.  SLAM-based Underwater Adaptive Sampling Using Autonomous Vehicles , 2018, OCEANS 2018 MTS/IEEE Charleston.

[3]  Vijayan K. Asari,et al.  The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches , 2018, ArXiv.

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Dan Brown,et al.  An approaches for noise induced object classifications accuracy improvement , 2019 .

[6]  Wenjie Chen,et al.  Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters , 2019, WUWNet.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Nitin Khanna,et al.  DCT-domain Deep Convolutional Neural Networks for Multiple JPEG Compression Classification , 2017, Signal Process. Image Commun..

[9]  Florent Perronnin,et al.  High-dimensional signature compression for large-scale image classification , 2011, CVPR 2011.

[10]  Alberto Quattrini Li,et al.  SVIn2: Sonar Visual-Inertial SLAM with Loop Closure for Underwater Navigation , 2018, ArXiv.

[11]  Huimin Lu,et al.  Contrast enhancement for images in turbid water. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Robert M. Gray,et al.  Lloyd clustering of Gauss mixture models for image compression and classification , 2005, Signal Process. Image Commun..

[16]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.