@article{ref1,
title="Real-time instance segmentation of traffic videos for embedded devices",
journal="Sensors (Basel)",
year="2021",
author="Panero Martinez, Ruben and Munteanu, Adrian and Cornelis, Bruno and Schiopu, Ionut",
volume="21",
number="1",
pages="e275-e275",
abstract="The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is  proposed using a multi-resolution feature extraction backbone and improved network  designs for the object detection and instance segmentation branches. A novel  post-processing method is introduced to ensure a reduced rate of false detection by  evaluating the quality of the output masks. An improved network training procedure  is proposed based on a novel label assignment algorithm. An ablation study on  speed-vs.-performance trade-off further modifies the two branches and replaces the  conventional ResNet-based performance-oriented backbone with a lightweight  speed-oriented design. The proposed architectural variations achieve real-time  performance when deployed on embedded devices. The experimental results demonstrate  that the proposed instance segmentation method for traffic videos outperforms the  you only look at coefficients algorithm, the state-of-the-art real-time instance  segmentation method. The proposed architecture achieves qualitative results with  31.57 average precision on the COCO dataset, while its speed-oriented variations  achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010275",
url="http://dx.doi.org/10.3390/s21010275"
}