STCNet

STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection

The code will be available soon.

Environment

Python 3.6

Pytorch 1.3+

Experiments

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

Visualization

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

Acknowledgements

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

Citation

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: http://arxiv.org/abs/2011.04863.

Contact

If you have any question, please feel free to contact me (Yichao Cao, [email protected]). Thanks :-)