TY - GEN
T1 - Real-time Semantic Segmentation for Aggregating Long-Range Information and Region Understanding
AU - Chen, Yifan
AU - Yan, Liping
AU - Xia, Yuanqing
AU - Xiao, Bo
N1 - Publisher Copyright:
© 2022 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2022
Y1 - 2022
N2 - With the improvement of hardware performance, semantic segmentation based on convolutional neural network has achieved wide concern for its' advantage of high accuracy. However, in practical application scenarios, such as autonomous driving, what we care about is not only the segmentation accuracy, but also the ability of the model to process information in real-time on edge devices. In this paper, a lightweight and efficient real-time semantic segmentation network was proposed, which is based on a two-way structure, aggregates detailed spatial information and high-level semantic information, integrates long-range dependencies and region-level understanding. The proposed algorithm achieves good performance. We conducted experiments based on an NVIDIA Geforce RTX 2080 SUPER graphics card and achieved 73.8% accuracy on Cityscapes dataset with a speed of 211 FPS, and 72.8% accuracy on Camvid dataset.
AB - With the improvement of hardware performance, semantic segmentation based on convolutional neural network has achieved wide concern for its' advantage of high accuracy. However, in practical application scenarios, such as autonomous driving, what we care about is not only the segmentation accuracy, but also the ability of the model to process information in real-time on edge devices. In this paper, a lightweight and efficient real-time semantic segmentation network was proposed, which is based on a two-way structure, aggregates detailed spatial information and high-level semantic information, integrates long-range dependencies and region-level understanding. The proposed algorithm achieves good performance. We conducted experiments based on an NVIDIA Geforce RTX 2080 SUPER graphics card and achieved 73.8% accuracy on Cityscapes dataset with a speed of 211 FPS, and 72.8% accuracy on Camvid dataset.
KW - Bilateral Neural Network
KW - Deep Learning
KW - Real-time Semantic Segmentation
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85140461843&partnerID=8YFLogxK
U2 - 10.23919/CCC55666.2022.9902015
DO - 10.23919/CCC55666.2022.9902015
M3 - Conference contribution
AN - SCOPUS:85140461843
T3 - Chinese Control Conference, CCC
SP - 6464
EP - 6469
BT - Proceedings of the 41st Chinese Control Conference, CCC 2022
A2 - Li, Zhijun
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 41st Chinese Control Conference, CCC 2022
Y2 - 25 July 2022 through 27 July 2022
ER -