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Training method and detection method of electric wire based on deep separable convolutional neural network

A technology of convolutional neural network and training method, which is applied in the field of image processing and pattern recognition, can solve problems such as the inability to guarantee that the network has learned the characteristics of wires, and achieve the effects of strong interpretability, easy processing, and fast calculation speed

Active Publication Date: 2021-09-07
SHANGHAI JIAOTONG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0007] 1) If you directly use the network to output whether there are wires on the picture, you need to limit the size of the network input, not only will the picture be scaled and lost information, but also cannot guarantee whether the network has actually learned the characteristics of the wire

Method used

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  • Training method and detection method of electric wire based on deep separable convolutional neural network
  • Training method and detection method of electric wire based on deep separable convolutional neural network
  • Training method and detection method of electric wire based on deep separable convolutional neural network

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Embodiment

[0047]This embodiment provides a wire training method and detection method based on a small-scale separate convolutional neural network. The general idea includes 4 steps in the training part and 5 steps in the detection part:

[0048] Training part:

[0049] S1, building a training network;

[0050] S2, prepare the grayscale single-channel image of the wire, and the corresponding binary mask truth map;

[0051] S3, grouping 13×13 slices centered on each pixel;

[0052] S4, input the slice into network training to obtain network weight parameters.

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Abstract

A wire training and detection method based on a convolutional neural network and adopting a depthwise separable convolution structure, including: constructing a neural network using depthwise separable convolution; using uniformly distributed small slices to train convolution kernels; The convolution kernel is used for feature extraction of infrared grayscale images; the image is binarized according to the threshold value, small area areas are removed, and straight line areas are connected using the probability Hough transform method. The present invention trains the convolution kernel capable of extracting the features of infrared wires through the method of machine learning, which can effectively extract the features of the wires from the infrared grayscale image; combined with morphological processing and linear Hough transform, the wires can be finally processed in real time detection.

Description

technical field [0001] The invention relates to the technical field of image processing and pattern recognition, in particular to a training method and detection method for electric wires based on a deep separable convolutional neural network. Background technique [0002] Safety issues in helicopter flight have always been a hot topic in the industry. Existing helicopter obstacle avoidance mainly relies on manual visual observation, limited by the resolution distance and resolution ability of human eyes, small obstacles such as high-voltage wires are not easy to be detected. In addition, the pilot needs to be very concentrated to find obstacles such as high-voltage wires ahead, which greatly increases the work intensity and mental pressure of the pilots. The helicopter automatic obstacle avoidance system based on optical, radar and infrared technologies can improve the flight safety obstacle avoidance ability of aircraft under low visibility conditions. It is an advantageo...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06V20/20G06V10/462G06N3/045
Inventor 李元祥刘嘉玮龚政庹红娅周拥军
Owner SHANGHAI JIAOTONG UNIV