Name
Abstract | ||
|---|---|---|
Industrial smoke emissions present a serious threat to natural ecosystems and human health. Prior works have shown that using computer vision techniques to identify smoke is a low-cost and convenient method. However, translucent smoke detection is a challenging task because of the irregular contours and complex motion state. To overcome these problems, we propose a novel spatiotemporal cross network (STCNet) to recognize industrial smoke emissions. The proposed STCNet involves a spatial pathway to extract appearance features and a temporal pathway to capture smoke motion information. Our STCNet is more targeted and goal oriented for dealing with translucent, nonrigid smoke objects. The spatial path can easily recognize obvious nonsmoking objects such as trees and buildings, and the temporal path can highlight the obscure traces of motion smoke. Our STCNet achieves the mutual guidance of multilevel spatiotemporal information by bidirectional feature fusion on multilevel feature maps. Extensive experiments on public datasets show that our STCNet achieves clear improvements against the best competitors by 6.2%. We also perform in-depth ablation studies on STCNet to explore the impacts of different feature fusion methods for the entire model. The code will be available at
https://github.com/Caoyichao/STCNet
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Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11042-021-11766-3 | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Smoke detection, Video understanding, Deep learning | Journal | 81 |
Issue | ISSN | Citations |
7 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yichao Cao | 1 | 4 | 1.73 |
| Qingfei Tang | 2 | 1 | 1.36 |
| Xiaobo Lu | 3 | 141 | 25.71 |
| Li Fan | 4 | 0 | 1.01 |
| Jinde Cao | 5 | 0 | 2.70 |