An Automatic Information Extraction Method for the Combustion Flame of Chain Furnace Based on U-net

Zhang, Peisen; Tan, Jianwen; Liu, Hei

doi:10.1109/tocs53301.2021.9688933

Cited by 1 publication

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“…Choi H. S. et al [18] applied the FCN model to fire detection based on a FiSmo dataset. Cai C et al [19] used the U-Net model to segment flames of a chain furnace. Although the above work successfully applied fully convolutional models to fire segmentation tasks, since the performance of the same model on different targets will be quite different, these models that perform well in respective scenarios are not necessarily suitable for forest fire segmentation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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In recent years, frequent forest fires have plagued countries all over the world, causing serious economic damage and human casualties. Faster and more accurate detection of forest fires and timely interventions have become a research priority. With the advancement in deep learning, fully convolutional network architectures have achieved excellent results in the field of image segmentation. More researchers adopt these models to segment flames for fire monitoring, but most of the works are aimed at fires in buildings and industrial scenarios. However, there are few studies on the application of various fully convolutional models to forest fire scenarios, and comparative experiments are inadequate. In view of the above problems, on the basis of constructing the dataset with remote-sensing images of forest fires captured by unmanned aerial vehicles (UAVs) and the targeted optimization of the data enhancement process, four classical semantic segmentation models and two backbone networks are selected for modeling and testing analysis. By comparing inference results and the evaluation indicators of models such as mPA and mIoU, we can find out the models that are more suitable for forest fire segmentation scenarios. The results show that the U-Net model with Resnet50 as a backbone network has the highest segmentation accuracy of forest fires with the best comprehensive performance, and is more suitable for scenarios with high-accuracy requirements; the DeepLabV3+ model with Resnet50 is slightly less accurate than U-Net, but it can still ensure a satisfying segmentation performance with a faster running speed, which is suitable for scenarios with high real-time requirements. In contrast, FCN and PSPNet have poorer segmentation performance and, hence, are not suitable for forest fire detection scenarios.

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Section: Introductionmentioning

confidence: 99%