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Author: search.filters.author.Kaitwanidvilai, SomyotSubject: search.filters.subject.Convolutional neural network

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    Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks
    (2021-01-01)
    Gokaraju, Jnana Sai Abhishek Varma
    ;
    Song, Weon Keun
    ;
    Ka, Min Ho
    ;
    Kaitwanidvilai, Somyot
    The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.
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    A convolutional neural network for segmentation of background texture and defect on copper clad lamination surface
    (2018-08-13)
    Sison, Harn
    ;
    Konghuayrob, Poom
    ;
    Kaitwanidvilai, Somyot
    This research interprets the design and test process of copper clad lamination surface defects detection. The system was included four following stages: Image acquisition, image pre-processing and segmentation, convolutional neural network design and image classification. Image processing method and pattern recognition algorithm are utilized in the system. First, the author applies the smoothing filters to eliminate noise from the images and segmenting a defect from background texture. Then, the convolutional neural network architecture is created to learn local feature of defect and background texture. Finally, defect and background images from segmentation step are collected and fed into a convolutional neural network to train and perform the classification task. The classification results demonstrate that the proposed method can re-checked false positive detect from the conventional Sobel edge detection, Hence the accuracy was increased from 78.1% to 98.2%.