STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
The code will be available soon.
Python 3.6
Pytorch 1.3+
F-Scores for some methods on RISE dataset.
| Model | S0 | S1 | S2 | S3 | S4 | S5 | Average |
|---|---|---|---|---|---|---|---|
| Flow-SVM | .42 | .59 | .47 | .63 | .52 | .47 | .517 |
| Flow-I3D | .55 | .58 | .51 | .68 | .65 | .50 | .578 |
| RGB-SVM | .57 | .70 | .67 | .67 | .57 | .53 | .618 |
| RGB-I3D | .80 | .84 | .82 | .87 | .82 | .75 | .817 |
| RGB-I3D-ND | .76 | .79 | .81 | .86 | .76 | .68 | .777 |
| RGB-I3D-FP | .76 | .81 | .82 | .87 | .81 | .71 | .797 |
| RGB-I3D-TSM | .81 | .84 | .82 | .87 | .80 | .74 | .813 |
| RGB-I3D-LSTM | .80 | .84 | .82 | .85 | .83 | .74 | .813 |
| RGB-I3D-NL | .81 | .84 | .83 | .87 | .81 | .74 | .817 |
| RGB-I3D-TC | .81 | .84 | .84 | .87 | .81 | .77 | .823 |
| Plain SE-Resnext | .83 | .82 | .84 | .85 | .78 | .83 | .826 |
| STCNet(MobileNetv2) | .86 | .88 | .87 | .89 | .84 | .86 | .868 |
| STCNet(SE-ResNext) | .88 | .89 | .90 | .90 | .86 | .88 | .885 |
Compare with other methods on RISE dataset. (RTX2080Ti GPU)
| Model | Backbone | Params | Flops | Latency | Throughput | Average |
|---|---|---|---|---|---|---|
| RGB-I3D | Inception I3D | 12.3M | 62.7G | 30.56ms | 32.71vid/s | .817 |
| RGB-I3D-TSM | Inception I3D | 12.3M | 62.7G | 31.85ms | 31.40vid/s | .813 |
| RGB-I3D-LSTM | Inception I3D | 38.0M | 62.9G | 31.01ms | 32.25vid/s | .813 |
| RGB-I3D-NL | Inception I3D | 12.3M | 62.7G | 30.32ms | 32.98vid/s | .817 |
| RGB-I3D-TC | Inception I3D | 12.3M | 62.7G | 30.41ms | 32.88vid/s | .823 |
| Plain SE-Resnext | SE-ResNeXt-50 | 26.6M | 34.4G | 22.10ms | 45.25vid/s | .826 |
| STCNet (Proposed) | Mobilenetv2 | 3.7M | 2.4G | 9.12ms | 109.7vid/s | .868 |
| STCNet (Proposed) | SE-ResNeXt-50 | 27.2M | 34.6G | 23.49ms | 42.57vid/s | .885 |
Input RGB frames (the top row) in RISE dataset and corresponding residual frames (the bottom row)
Grad-CAM visualization for spatial and temporal pathway.
GRAD-CAM visualization of Spatial path:
GRAD-CAM visualization of Temporal path:
False positive cases in the testing set.
False negative cases in the testing set.
Each GIF has the same name as the original video. If interested, you can check the corresponding original video in RISE dataset: https://github.com/CMU-CREATE-Lab/deep-smoke-machine
We thank Carnegie Mellon University (CMU) and Pennsylvania State University (PSU) for their efforts in environmental protection. We also thank the Big Data Center of Southeast University for providing the facility support on the numerical calculations in this paper.
And this is a good implementation for our method: https://github.com/ChangyWen/STCNet-for-Smoke-Detection
If you use our code or paper, please cite:
Y. Cao, Q. Tang, X. Lu, F. Li, and J. Cao, “STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection,” arXiv:2011.04863 [cs], Nov. 2020, Accessed: Nov. 16, 2020. [Online]. Available: https://arxiv.org/abs/2011.04863.
If you have any question, please feel free to contact me (Yichao Cao, [email protected]). Thanks :-)