Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21010275 | SENSORS |
Keywords | DocType | Volume |
real-time instance segmentation, deep neural network, embedded devices | Journal | 21 |
Issue | ISSN | Citations |
1 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruben Panero Martinez | 1 | 0 | 0.34 |
| I. Schiopu | 2 | 37 | 8.04 |
| Bruno Cornelis | 3 | 48 | 11.06 |
| Adrian Munteanu | 4 | 664 | 80.29 |