Browsing by Author "Wu, Bo-Xun"

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Summary Embedded Deep Learning Object Detection Model Competition
(2019-01-01)
Guo, Jiun-In
;
Tsai, Chia-Chi
;
Yang, Yong-Hsiang
;
Lin, Hung-Wei
;
Wu, Bo-Xun
The embedded deep learning object detection model competition in IEEE MMSP2019 focuses on the object detection for sensing technology in autonomous driving vehicles, which aims at detecting small objects in worse conditions through embedded systems. We provide a dataset with 89,002 annotated images for training and 1,500 annotated images for validation. We test participants' models through 6,000 testing images, which are separated into 3,000 for qualification and 3,000 for finals. There are 87 teams of participants registered this competition and 14 teams submitted the team composition. At last there are nine teams entering the final competition and five teams submitting their final models that can be realized in NVIDIA Jetson TX-2. At the end, only one team's model passed the target accuracy requirement for grading and became the champion of the contest, which the winner is team R.JD.
Traffic Sign Recognition with Light Convolutional Networks
(2018-01-01)
Wu, Bo-Xun
;
Wang, Pin-Yu
;
Yang, Yi-Ta
;
Guo, Jiun-In
;
電子工程學系及電子研究所
In this work, we aim to design a light net that can be executed on the embedded system in real time. We modify VGG Net to a small net, called Safe Net, and utilize multi-scale features for traffic sign recognition. Moreover, we convert the dataset into grayscale, which has been proved that has a better performance on GTSRB dataset. In addition, we augment the training data by about 6.6 times more via spinning, distorting and flipping to boost the accuracy. On Nvidia Jetson TX1, Safe Net only takes 4.58ms per image including preprocessing at the testing and Safe Net can even achieve 99.34% accuracy.