Browsing by Author "Lo, Shao-Yuan"

Now showing 1 - 3 of 3

Efficient Road Lane Marking Detection with Deep Learning
(2018-01-01)
Chen, Ping-Rong
;
Lo, Shao-Yuan
;
Hang, Hsueh-Ming
;
Chan, Sheng-Wei
;
Lin, Jing-Jhih
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using deep convolutional neural network to extract robust lane marking features. To improve its performance with a target of lower complexity, the dilated convolution is adopted. A shallower and thinner structure is designed to decrease the computational cost. Moreover, we also design post-processing algorithms to construct 3rd-oder polynomial models to fit into the curved lanes. Our system shows promising results on the captured road scenes.
INCORPORATING LUMINANCE, DEPTH AND COLOR INFORMATION BY A FUSION-BASED NETWORK FOR SEMANTIC SEGMENTATION
(2019-01-01)
Hung, Shang-Wei
;
Lo, Shao-Yuan
;
Hang, Hsueh-Ming
;
交大名義發表
;
電子工程學系及電子研究所
Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays; thus, depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes. LDFNet outperforms the other state-of-art systems on the Cityscapes dataset, and its inference speed is faster than most of the existing networks. The experimental results show the effectiveness of the proposed multi-modal fusion network and its potential for practical applications.
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
(2019-01-01)
Lo, Shao-Yuan
;
Hang, Hsueh-Ming
;
Chan, Sheng-Wei
;
Lin, Jing-Jhih
;
交大名義發表
Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes. In this paper, we focus on the problem of multi-class lane semantic segmentation. Based on the observation that the lane is a small-size and narrow-width object in a road scene image, we propose two techniques, Feature Size Selection (FSS) and Degressive Dilation Block (DD Block). The FSS allows a network to extract thin lane features using appropriate feature sizes. To acquire fine-grained spatial information, the DD Block is made of a series of dilated convolutions with degressive dilation rates. Experimental results show that the proposed techniques provide obvious improvement in accuracy, while they achieve the same or faster inference speed compared to the baseline system, and can run at real-time on high-resolution images.