Browsing by Author "Tseng, Ching-Kan"

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Embedded Multiple Object Detection Based on Deep Learning Technique for Advanced Driver Assistance System
(2017-01-01)
Chang, Fong-An
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Tsai, Chia-Chi
;
Tseng, Ching-Kan
;
Guo, Jiun-In
;
電子工程學系及電子研究所
This paper proposes an optimized pedestrian and vehicle detection method based on deep learning technique. We optimize the convolutional neural network architecture by three mainly methods. The first one is the choice of the learning policy. The second one is to simplify the convolutional neural network architecture. The last one is careful choice of training samples. With limited loss of accuracy, we can greatly speed up the original deep learning method coming from CAFFE. The proposed system is developed on PCs and implemented on the platforms of both the PC and embedded systems. We can achieve around 90% accuracy when it is tested on an open-source dataset. On PCs with Intel i7@3.5GHz CPU, the proposed design can reach the performance about 720x480 video at 25 frames per second. On the NVIDIA JETSON TX1 embedded system, the proposed design can reach the performance about 720x480 video at 5 frames per second.
Vehicle Detection and Classification based on Deep Neural Network for Intelligent Transportation Applications
(2018-01-01)
Tsai, Chia-Chi
;
Tseng, Ching-Kan
;
Tang, Ho-Chia
;
Guo, Jiun-In
;
電子工程學系及電子研究所
This paper proposes an optimized vehicle detection and classification method based on deep learning technology for intelligent transportation applications. We optimize the Convolutional Neural Network (CNN) architecture by fine-tuning the existing CNN architecture for the intelligent transportation applications. The proposed design achieves the accuracy of miss rate around 10% when FPPI is 0.1. Realized on nVidia Titan-X GPU, the proposed design can reach the performance about 720x480 video under different weather condition (day, night, raining) at 25fps. The proposed model can achieve 90% accuracy on three target vehicle classes including small vehicles (Sedan, SUV, Van), big vehicles (Bus) and Trucks.