FCSNet: 频域感知的跨特征融合烟雾分割网络

王开正; 曾瑶; 张占喜; 谭义章; 文刚

doi:10.11999/JEIT241021

FCSNet: 频域感知的跨特征融合烟雾分割网络

doi: 10.11999/JEIT241021 cstr: 32379.14.JEIT241021

1.
昆明理工大学电力工程学院昆明 650500
2.
南方电网电动汽车服务有限公司深圳 518000
3.
云南电网有限责任公司电力科学研究院昆明 650214

基金项目: 国家自然科学基金(52107017)，云南省科技厅基础研究专项青年项目(202201AU070172)

详细信息

作者简介:
王开正：男，副教授，研究方向为电网防灾减灾、电力装备绝缘失效机制与防御技术

曾瑶：男，硕士生，研究方向为电网防灾减灾、电力装备绝缘失效机制与防御技术

张占喜：男，硕士，研究方向为深度学习、目标分割

谭义章：男，硕士生，研究方向为电网防灾减灾、电力装备绝缘失效机制与防御技术

文刚：男，硕士，研究方向为电网防灾检测、深度学习

通讯作者:
王开正　kz.wang@foxmail.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2024-11-14
- 修回日期: 2025-05-28
- 网络出版日期: 2025-06-10
- 刊出日期: 2025-07-22

FCSNet: A Frequency-Domain Aware Cross-Feature Fusion Network for Smoke Segmentation

1.
School of Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
2.
Southern Power Grid Electric Vehicle Service Co., Ltd., Shenzhen 518000, China
3.
Electric Power Research Institute of Yunnan Electric Power Company, Kunming 650214, China

Funds: The National Natural Science Foundation of China (52107017), Yunnan Provincial Department of Science and Technology Basic Research Special Youth Project (202201AU070172)

摘要

摘要: 由于烟雾具有非刚性结构、半透明性以及形态多变等特点，烟雾的语义分割相较于其他物体具有更大的挑战性。为此，该文设计了频域感知的跨特征融合烟雾分割网络(FCSNet)，用于应对真实场景中的烟雾分割任务。该网络由Transformer分支、卷积神经网络分支、特征融合分支以及多级高频感知分支组成。为了在获取全局上下文信息的同时降低Transformer分支的计算复杂度，提出频率Transformer分支，该分支基于傅里叶变换来获取全局特征，并使用频域中的低频幅值来代表大规模的语义结构。此外，还提出域间交互模块(DIM)，该模块通过加权融合操作和坐标注意力机制，促进了来自不同特征源信息的协同学习和整合，有效地融合全局和局部信息。为了充分利用边缘信息的固有分割能力来解决混淆区域的不准确分割问题，提出多级高频感知模块(MHFM)，以获取准确的高频边缘信息。考虑到烟雾的非刚性特征，设计多向交叉注意力模块(MHCA)，该模块计算边缘特征图和解码器特征图之间的相似性，从而指导混淆区域的分割结果。实验结果表明，该网络在真实场景烟雾分割数据集中的两个测试数据集上分别达到了58.95%和63.92%的平均交并比，在SMOKE5K数据集上达到了78.94%的平均交并比。与其他方法相比，该网络能够获得准确的烟雾定位和更精细的烟雾边缘。
- 烟雾语义分割 /
- 频率Transformer /
- 域间交互 /
- 多级高频感知 /
- 多向交叉注意力
Abstract: Objective Vision-based smoke segmentation enables pixel-level classification of smoke regions, providing more spatially detailed information than traditional bounding-box-based detection approaches. Existing segmentation models based on Deep Convolutional Neural Networks (DCNNs) demonstrate reasonable performance but remain constrained by a limited receptive field due to their local inductive bias and two-dimensional neighborhood structure. This constraint reduces their capacity to model multi-scale features, particularly in complex visual scenes with diverse contextual elements. Transformer-based architectures address long-range dependencies but exhibit reduced effectiveness in capturing local structure. Moreover, the limited availability of real-world smoke segmentation datasets and the underutilization of edge information reduce the generalization ability and accuracy of current models. To address these limitations, this study proposes a Frequency-domain aware Cross-feature fusion Network for Smoke segmentation (FCSNet), which integrates frequency-domain and spatial-domain representations to enhance multi-scale feature extraction and edge information retention. A dataset featuring various smoke types and complex backgrounds is also constructed to support model training and evaluation under realistic conditions. Methods To address the challenges of smoke semantic segmentation in real-world scenarios, this study proposes FCSNet, a frequency-domain aware cross-feature fusion network. Given the high computational cost associated with Transformer-based models, a Frequency Transformer is designed to reduce complexity while retaining global representation capability. To overcome the limited contextual modeling of DCNNs and the insufficient local feature extraction of Transformers, a Domain Interaction Module (DIM) is introduced to facilitate effective fusion of global and local information. Within the network architecture, the Frequency Transformer branch extracts low-frequency components to capture large-scale semantic structures, thereby improving global scene comprehension. In parallel, a Multi-level High-Frequency perception Module (MHFM) is combined with Multi-Head Cross Attention (MHCA). MHFM processes multi-layer encoder features to capture high-frequency edge details at full resolution using a shallow structure. MHCA then computes directional global similarity maps to guide the decoder in aggregating contextual information more effectively. Results and Discussions The effectiveness of FCSNet is evaluated through comparative experiments against state-of-the-art methods using the RealSmoke and SMOKE5K datasets. On the RealSmoke dataset, FCSNet achieves the highest segmentation accuracy, with mean Intersection over Union (mIoU) values of 58.59% on RealSmoke-1 and 63.92% on RealSmoke-2, outperforming all baseline models (Table 4). Although its FLOPs are slightly higher than those of TransFuse, FCSNet demonstrates a favorable trade-off between accuracy and computational complexity. Qualitative results further highlight its advantages under challenging conditions. In scenes affected by clouds, fog, or building occlusion, FCSNet distinguishes smoke boundaries more clearly and reduces both false positives and missed detections (Fig. 8). Notably, in RealSmoke-2, which contains fine and sparse smoke patterns, FCSNet exhibits superior performance in smoke localization and edge detail segmentation compared to other methods (Fig. 9). On the SMOKE5K dataset, FCSNet achieves an mIoU of 78.94%, showing a clear advantage over competing algorithms (Table 5). Visual comparisons also indicate that FCSNet generates more accurate and refined smoke boundaries (Fig. 10). These results confirm that FCSNet maintains strong segmentation accuracy and robustness across diverse real-world scenes, supporting its generalizability and practical utility in smoke detection tasks. Conclusions To address the challenges of smoke semantic segmentation in real-world environments, this study proposes FCSNet, a network that integrates frequency- and spatial-domain information. A Frequency Transformer is introduced to reduce computational cost while enhancing global semantic modeling through low-frequency feature extraction. To compensate for the limited receptive field of DCNNs and the local feature insensitivity of Transformers, a DIM is designed to fuse global and local representations. An MHFM is employed to extract edge features, improving segmentation performance in ambiguous regions. Additionally, an MHCA mechanism aligns high-frequency edge features with decoder representations to guide segmentation in visually confusing areas. By jointly leveraging low-frequency semantics and high-frequency detail, FCSNet achieves effective fusion of contextual and structural information. Extensive quantitative and qualitative evaluations confirm that FCSNet performs robustly under complex interference conditions, including clouds, fog, and occlusions, enabling accurate smoke localization and fine-grained segmentation.
- Smoke semantic segmentation /
- Frequency Transformer /
- Domain interaction /
- Multi-level high-frequency perception /
- Multi-Head Cross Attention (MHCA)

HTML全文

图 1 烟雾分割网络

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图 2 不同的Transformer架构

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图 3 傅里叶频谱解耦示意图

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图 4 域间交互模块的框架

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图 5 MHFM的详细结构

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图 6 MHCA的结构

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图 7 空间注意力图的可视化

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图 8 在RealSmoke-1数据集上的结果

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图 9 在RealSmoke-2数据集上的结果

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图 10 在SMOKE5K数据集上的结果

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表 1 频率Transformer的消融实验

方法	mIoU (%)		参数量 (M)	FLOPs (G)	fps
方法	RealSmoke-1	RealSmoke-2	参数量 (M)	FLOPs (G)	fps
Model1	58.81	64.89	23.67	11.95	28.34
Model2	56.12	58.37	19.16 (19.05%)	11.11 (7.03%)	30.93
Model3	57.74	61.74	20.29 (14.28%)	11.11 (7.03%)	30.15
本文FCSNet	58.59(0.22)	63.92(0.97)	19.72 (16.69%)	11.11 (7.03%)	30.49

下载: 导出CSV

表 2 DIM的消融实验

方法	mIoU (%)
方法	RealSmoke-1	RealSmoke-2
Model1	53.78	51.23
Model2	54.73	52.81
Model3	58.15	55.31
本文FCSNet	58.59	63.92

下载: 导出CSV

表 3 MHFM和MHCA的消融实验

方法	mIoU (%)
方法	RealSmoke-1	RealSmoke-2
Model1	53.51	54.32
Model2	55.87	58.69
Model3	56.53	57.53
Model4	58.21	59.19
本文FCSNet	58.59	63.92

下载: 导出CSV

表 4 在RealSmoke数据集上的烟雾分割性能

方法	mIoU (%)		参数量 (M)	FLOPs (G)
方法	RealSmoke-1	RealSmoke-2	参数量 (M)	FLOPs (G)
FCN8s	52.47	54.32	14.42	19.69
W-Net	56.63	57.53	31.75	12.50
DMAENet	56.86	58.37	32.61	44.08
Trans-BVM	58.17	63.24	51.62	/
DSS	50.46	52.32	28.80	21.62
TransUnet	54.56	61.32	75.65	22.23
TransFuse	53.71	61.56	25.02	8.44
本文FCSNet	58.59	63.92	19.72	11.11

下载: 导出CSV

表 5 在SMOKE5K数据集上的分割性能

方法	mIoU (%)
FCN8s	71.39
W-Net	74.65
Trans-BVM	77.69
DSS	68.87
TransFuse	75.87
DMAENet	76.06
本文FCSNet	78.94

下载: 导出CSV

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