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.
Panero Martinez, R , Schiopu, I , Cornelis, B & Munteanu, A 2021, ' Real-Time Instance Segmentation of Traffic Videos for Embedded Devices ', Sensors , vol. 21, no. 1, 275.
Panero Martinez, R. , Schiopu, I. , Cornelis, B. , & Munteanu, A. (2021). Real-Time Instance Segmentation of Traffic Videos for Embedded Devices . Sensors , 21 (1), [275].
@article{f55404cb6fe84b1b9a85a260d1bcb7b4,
title = " Real-Time Instance Segmentation of Traffic Videos for Embedded Devices " ,
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. " ,
keywords = " instance segmentation, real-time, deep neural networks, embedded devices " ,
author = " {Panero Martinez}, Ruben and Ionut Schiopu and Bruno Cornelis and Adrian Munteanu " ,
note = " Copyright: This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine " ,
year = " 2021 " ,
month = jan,
day = " 3 " ,
doi = " 10.3390/s21010275 " ,
language = " English " ,
volume = " 21 " ,
journal = " Sensors " ,
issn = " 1424-8220 " ,
publisher = " Multidisciplinary Digital Publishing Institute (MDPI) " ,
number = " 1 " ,
}