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Semantic Segmentation of Fire and Smoke Images Based on Dual Attention Mechanism
Fire and smoke recognition has a wide range of applications in fire warning, anti-jamming, battlefield situational awareness and other fields. Using the method of semantic segmentation for fire and smoke recognition can obtain the fire and smoke area of the image more accurately and comprehensively. Although the DeepLabV3+ network based on the Encoder-decoder structure has achieved excellent performance in semantic segmentation in many fields, it still has some shortcomings, such as slow fitting, blurred edge segmentation, and holes in the feature map. In order to address the deficiencies of DeepLabV3+,In this paper we propose a novel method of introducing parallel dual attention mechanism network(DAMN). We designed a parallel connection between DAMN and atous spatial pyramid pooling(ASPP). DAMN and ASPP process the feature map generated by the network in parallel, and then fuse the feature map information generated by DAMN and ASPP module. Meanwhile, we built a multi-scene fire and smoke dataset named “Fire-Smoke” for comprehensive assessment in fire and smoke image recognition. Fire-Smoke contains over 8,000 hand-labeled smoke labels. We achieve better segmentation performance on Fire-Smoke dataset. In particular, it achieves an average IoU score of 85.77% on the Fire-Smoke test set, an improvement of 2.18% over the DeepLabV3+.
Semantic Segmentation of Fire and Smoke Images Based on Dual Attention Mechanism
Fire and smoke recognition has a wide range of applications in fire warning, anti-jamming, battlefield situational awareness and other fields. Using the method of semantic segmentation for fire and smoke recognition can obtain the fire and smoke area of the image more accurately and comprehensively. Although the DeepLabV3+ network based on the Encoder-decoder structure has achieved excellent performance in semantic segmentation in many fields, it still has some shortcomings, such as slow fitting, blurred edge segmentation, and holes in the feature map. In order to address the deficiencies of DeepLabV3+,In this paper we propose a novel method of introducing parallel dual attention mechanism network(DAMN). We designed a parallel connection between DAMN and atous spatial pyramid pooling(ASPP). DAMN and ASPP process the feature map generated by the network in parallel, and then fuse the feature map information generated by DAMN and ASPP module. Meanwhile, we built a multi-scene fire and smoke dataset named “Fire-Smoke” for comprehensive assessment in fire and smoke image recognition. Fire-Smoke contains over 8,000 hand-labeled smoke labels. We achieve better segmentation performance on Fire-Smoke dataset. In particular, it achieves an average IoU score of 85.77% on the Fire-Smoke test set, an improvement of 2.18% over the DeepLabV3+.
Semantic Segmentation of Fire and Smoke Images Based on Dual Attention Mechanism
Wang, Yunpeng (author) / Luo, Zhenbao (author) / Chen, Daizhong (author) / Li, Yiqiang (author)
2022-12-02
2269138 byte
Conference paper
Electronic Resource
English
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