The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Myo-taeg Lim - One of the best experts on this subject based on the ideXlab platform.

  • Convolution neural network with selective multi-stage feature fusion: Case study on Vehicle rear detection
    Applied Sciences, 2018
    Co-Authors: Won Jae Lee, Dong W. Kim, Tae Koo Kang, Myo-taeg Lim
    Abstract:

    Vision-based Vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a Vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve Vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain Characteristic Vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a Vehicle detector. The experimental results verified that the proposed Vehicle detection framework exhibited better performance than previous frameworks.

Jian Zhong Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Study on the Characteristics of Air Spring
    Advanced Materials Research, 2013
    Co-Authors: Qi Yao Yang, Yu Ping, Jian Zhong Zhang
    Abstract:

    Due to of nonlinear performance and variable rigidity Characteristic Vehicle equipped with air suspension has more progress in the driving smoothness and handling stability. In the analysis based on the Characteristics of air spring and the principle of gas flow, the paper aims at obtaining the relation curve between stiffness of air spring and on/off time, therefore, the calculation of a example has been done, which provides the valuable basis for the bench tests of air spring and Vehicle control test.

Won Jae Lee - One of the best experts on this subject based on the ideXlab platform.

  • Convolution neural network with selective multi-stage feature fusion: Case study on Vehicle rear detection
    Applied Sciences, 2018
    Co-Authors: Won Jae Lee, Dong W. Kim, Tae Koo Kang, Myo-taeg Lim
    Abstract:

    Vision-based Vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a Vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve Vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain Characteristic Vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a Vehicle detector. The experimental results verified that the proposed Vehicle detection framework exhibited better performance than previous frameworks.

Qi Yao Yang - One of the best experts on this subject based on the ideXlab platform.

  • Study on the Characteristics of Air Spring
    Advanced Materials Research, 2013
    Co-Authors: Qi Yao Yang, Yu Ping, Jian Zhong Zhang
    Abstract:

    Due to of nonlinear performance and variable rigidity Characteristic Vehicle equipped with air suspension has more progress in the driving smoothness and handling stability. In the analysis based on the Characteristics of air spring and the principle of gas flow, the paper aims at obtaining the relation curve between stiffness of air spring and on/off time, therefore, the calculation of a example has been done, which provides the valuable basis for the bench tests of air spring and Vehicle control test.

Dong W. Kim - One of the best experts on this subject based on the ideXlab platform.

  • Convolution neural network with selective multi-stage feature fusion: Case study on Vehicle rear detection
    Applied Sciences, 2018
    Co-Authors: Won Jae Lee, Dong W. Kim, Tae Koo Kang, Myo-taeg Lim
    Abstract:

    Vision-based Vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a Vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve Vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain Characteristic Vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a Vehicle detector. The experimental results verified that the proposed Vehicle detection framework exhibited better performance than previous frameworks.