Abstract
Computer vision has emerged as a cost-effective and convenient solution for identifying hazardous smoke emissions in industrial settings. However, in practical scenarios, the performance of existing methods can be affected by complex smoke characteristics and fluctuating environmental factors. To address these challenges, we propose a novel detection model called ESTNet. ESTNet utilizes both smoke texture features and unique motion features to enhance smoke detection. The Shallow Feature Enhancement Module (SFE) specifically enhances the learning of smoke texture features. The Spatio-temporal Feature Learning Module (SFL) effectively differentiates smoke from other interfering factors, enabling the establishment of smoke spatio-temporal feature learning. Notably, this module can be easily integrated into existing 2D CNNs, making it a versatile plug-and-play component. Furthermore, to improve the representation of the video, we employ Multi-Temporal Spans Fusion (MTSF) to incorporate information from multiple frames. This fusion technique allows us to obtain a comprehensive feature representation of the entire video. Extensive experiments and visualizations are conducted, demonstrating the effectiveness of our proposed method with state-of-the-art competitors.
This work was supported by the Applied Basic Research Program Project of Liaoning Province under Grant 2023JH2/101600043 and the National Natural Science Foundation of China (No.61873048).
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Acknowledgements
This work was supported by the Applied Basic Research Program Project of Liaoning Province under Grant 2023JH2/101600043 and the National Natural Science Foundation of China (No.61873048).
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Du, S., Lv, Z., Wang, L., Zhao, J. (2024). ESTNet: Efficient Spatio-Temporal Network for Industrial Smoke Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_29
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