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- W4313134476 abstract "With the progress of computer technology, intelligent vehicles have developed rapidly and are widely used in national defense, scientific research, daily life and other fields. Among them, obstacle detection is the core problem of intelligent vehicle navigation. Convolutional Neural Network is a kind of feedforward network. Its artificial neurons can respond to the units within the coverage, including convolution layer and pooling layer, which has excellent performance in image processing. In response to the problem of inaccurate and poor robustness of traditional underwater disorder detection algorithms, this article proposes a kind of underwater obstacle detection algorithm based on Convolutional Neural Networks. which can accurately identify underwater obstacles and non-obstacles. These traditional ways have the shortcomings of low scientific and technological content, simple function and limited detection. In order to make the blind travel safely and conveniently, this paper studies the intelligent blind guidance system. Because the traditional machine learning algorithm needs to extract a large number of features manually, the training is time-consuming and laborious, and it is difficult to meet the real-time detection of the target. The system is composed of image acquisition module and obstacle detection module. The image acquisition module collects binocular images and transmits them to the obstacle detection module to process the collected image data and obtain accurate obstacle areas. The detection method performs median filtering on the collected original image. The binocular image is corrected according to the camera parameters. A new convolution kernel is designed and applied to convolution neural network structure to generate accurate disparity map. The improved Convolutional Neural Network is based on the original network, adjusting the convolution, adjusting the size and number of the convolutional kernel, the size of the pooling layer space, and the depth of the neural network, by increasing the pooling layer of the convolution layer, to increase the recognition rate of obstacles." @default.
- W4313134476 created "2023-01-06" @default.
- W4313134476 creator A5041454440 @default.
- W4313134476 creator A5042551057 @default.
- W4313134476 date "2022-07-15" @default.
- W4313134476 modified "2023-09-27" @default.
- W4313134476 title "Visual localization of underwater obstacles based on Convolutional Neural Network" @default.
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- W4313134476 doi "https://doi.org/10.1109/icbaie56435.2022.9985788" @default.
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