MobileNet investigation: its application and reproducing edge detectors using depth-wise separable convolution
Conference: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
12/17/2021 - 12/19/2021 at Shenyang, China
Proceedings: ICMLCA 2021
Pages: 6Language: englishTyp: PDF
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Authors:
Ai, Lun (International School, Beijing University of Posts and Telecommunications, Beijing, Beijing, China)
Luo, Zhen (Mathematics and the Information science Department, Henan University of Economics and Law, Zhengzhou, Henan, China)
Wang, Chunyang (College of Computer and Information Science College of Software, Southwest University, Chongqing, Chongqing, China)
Wu, Yifan (Information and Computing Science, North China University of Science and Technology, Changsha, Hunan, China)
Abstract:
MobileNet has a wide range of applications, and its depth-wise separable convolution deduced the computational cost dramatically compared to traditional convolution methods. In this study, experiments are carried out to examine depthwise separable convolution’s ability in terms of reproducing edge detector kernels. MobileNet’s depth-wise separable convolution achieves great performance through reproducing kernels based on our LpP metric. The visualization results show that random initialization can reduce the calculation amount of the model and have higher efficiency. This study also compares three models, including InceptionV3, ResNet50, and MobileNetV2, on face mask detection tasks. The experiment result indicates that the MobileNetV2 model achieved the highest accuracy of 99.08%. A ground truth analysis on a test image containing three different face mask situations is performed to further discuss the model performance of MobileNetV2.