Journal Article10.1016/J.NEUCOM.2019.01.084
Faster R-CNN for marine organisms detection and recognition using data augmentation
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TL;DR: Three data augmentation methods dedicated to underwater-imaging are proposed, the inverse process of underwater image restoration is used to simulate different marine turbulence environments, and perspective transformation and Illumination synthesis are proposed to simulateDifferent marine uneven illuminating environments.
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About: This article is published in Neurocomputing. The article was published on 14 Apr 2019. The article focuses on the topics: Object detection & Convolutional neural network.
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Citations
Deep learning-based visual detection of marine organisms: A survey
TL;DR: Deep learning-based visual detection of marine organisms (VDMO) has attracted rapidly increasing attention paid to marine organisms, thereby expected to significantly benefit ocean ecology as discussed by the authors , and a comprehensive survey of deep learning based VDMO techniques are comprehensively revisited from a systematic viewpoint.
37
Surface crack detection based on image stitching and transfer learning with pretrained convolutional neural network
TL;DR: A crack detection technology based on a convolutional neural network, GoogLeNet Inception V3, that can automatically study the characteristics of the object from the dataset, which can adapt to the complex real environment is presented.
34
A 3D Convolutional Neural Network for Bacterial Image Classification
T. S. R. Mhathesh,J. Andrew,K. Martin Sagayam,Lawrence Henesey +3 more
- 01 Jan 2021
TL;DR: The proposed CNN model classifies the bacterial and non-bacterial images effectively and provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc.
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A quantitative detection algorithm based on improved faster R-CNN for marine benthos
Yong Liu,Shengnan Wang +1 more
TL;DR: In this paper, a quantitative detection algorithm for marine benthos based on Faster R-CNN is proposed, where a convolution kernel adaptive selection unit is embedded in the backbone to enhance the feature extraction ability of network.
31
A Novel Underwater Image Enhancement Algorithm and an Improved Underwater Biological Detection Pipeline
TL;DR: This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network and a self-adaptive global histogram stretching algorithm was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality.
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