面向无人机与遥感影像的烟雾火焰目标提取方法

doi:10.13474/j.cnki.11-2246.2025.1202

测绘通报 ›› 2025, Vol. 0 ›› Issue (12): 7-14.doi: 10.13474/j.cnki.11-2246.2025.1202

• 学术研究 • 上一篇

面向无人机与遥感影像的烟雾火焰目标提取方法

刘晓栋^1,2, 赵晨萌^3,4,5,6, 任迎华⁷, 杨礼平^1,2, 赵立科^1,2, 张卡^3,4,5,6

1. 江苏省地质调查研究院, 江苏南京 210018;
2. 自然资源江苏省卫星应用技术中心, 江苏南京 210018;
3. 气候系统预测与变化应对全国重点实验室(南京师范大学), 江苏南京 210023;
4. 南京师范大学地理科学学院, 江苏南京 210023;
5. 虚拟地理环境教育部重点实验室(南京师范大学), 江苏南京 210023;
6. 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023;
7. 江苏苏州地质工程勘察院, 江苏苏州 215129

收稿日期:2025-04-30 发布日期:2025-12-31
通讯作者: 张卡。E-mail:zhangka81@126.com
作者简介:刘晓栋(1978—),男,高级工程师,从事自然资源调查监测方面的应用研究。E-mail:21020261@qq.com
基金资助:
国家自然科学基金(42271342);江苏高校优势学科建设工程(164320H116)

The method of smoke and flame target extraction facing UAV and remote sensing images

LIU Xiaodong^1,2, ZHAO Chenmeng^3,4,5,6, REN Yinghua⁷, YANG Liping^1,2, ZHAO Like^1,2, ZHANG Ka^3,4,5,6

1. Geological Survey of Jiangsu Province, Nanjing 210018, China;
2. Natural Resources Satellite Application Technology Center of Jiangsu Province, Nanjing 210018, China;
3. State Key Laboratory of Climate System Prediction and Risk Management (Nanjing Normal University), Nanjing 210023, China;
4. School of Geography, Nanjing Normal University, Nanjing 210023, China;
5. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China;
6. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
7. Jiangsu Suzhou Geological Engineering Investigation Institute, Suzhou 215129, China

Received:2025-04-30 Published:2025-12-31

摘要/Abstract

摘要： 针对无人机与遥感影像烟雾火焰检测任务中存在的多尺度特征捕捉不足、复杂背景干扰及边缘模糊等瓶颈问题,本文提出了一种基于改进型YOLOv12的烟雾火焰目标提取方法。该方法通过混合局部通道注意力机制强化多尺度特征融合,利用自适应下采样模块改善低分辨率影像的细节保留能力,并设计自适应损失函数提升边界回归精度;结合微调SAM 2.1模型,可实现检测框内目标的像素级分割。在FASDD＿UAV、FASDD＿RS及S-Firedata数据集上的试验表明,本文方法的mAP₅₀指标分别达到93.1%、78.5%和68.2%,较基准模型YOLOv12分别提升了1.3、1.5和1.2个百分点,在小目标检测、遮挡场景及复杂光照条件下表现出显著优势;另外,消融试验证实MLCA与ADown模块的特征增强效果,以及Focaler-PIoU通过动态梯度分配对模型性能的优化作用。

关键词: 遥感影像, 烟雾火焰检测, YOLO, SAM, 注意力机制

Abstract: Addressing the bottlenecks in UAV and remote sensing-based smoke/flame detection task,such as insufficient multi-scale feature capture,complex background interference,and blurred edges,a method of smoke and flame target extraction based on the improved YOLOv12 model is proposed in this paper.The proposed method enhances multi-scale feature fusion through a mixed local-channel attention (MLCA)mechanism,improves detail retention in low-resolution images via an adaptive downsampling (ADown)module,and refines boundary regression accuracy with a customized adaptive loss function.Furthermore,by integrating the fine-tuned SAM2.1 model,the paper's method can realize pixel-level segmentation of targets within detection boxes.Experiments on the FASDD＿UAV,FASDD＿RS,and S-Firedata datasets shows that the proposed method achieves mAP50 scores of 93.1%,78.5%,and 68.2%,outperforming the baseline model YOLOv12 by 1.3%,1.5%,and 1.2%,respectively.The proposed method demonstrates significant advantages in detecting small targets,handling occluded scenarios and complex lighting conditions.Additionally,ablation experiments have confirmed the feature enhancement effects of the MLCA and Adown modules,as well as the optimization effect of Focaler-PIoU on model performance through dynamic gradient allocation.

Key words: remote sensing images, smoke flame detection, YOLO, SAM, attention mechanism

中图分类号:

P237

刘晓栋, 赵晨萌, 任迎华, 杨礼平, 赵立科, 张卡. 面向无人机与遥感影像的烟雾火焰目标提取方法[J]. 测绘通报, 2025, 0(12): 7-14.

LIU Xiaodong, ZHAO Chenmeng, REN Yinghua, YANG Liping, ZHAO Like, ZHANG Ka. The method of smoke and flame target extraction facing UAV and remote sensing images[J]. Bulletin of Surveying and Mapping, 2025, 0(12): 7-14.

参考文献

[1] RAFFERTY J P.Los Angeles wildfires of 2025[EB/OL].[2025-03-01].https://www.britannica.com/event/Los-Angeles-wildfires-of-2025.
[2] 赵柔嘉, 姚顺雨, 庞兴航.内蒙古森林消防:转型强能全力守护北疆万里绿色长城[J].中国安全生产, 2024, 19(8):38-39.
[3] 方超颖, 许军, 丁志龙, 等.山火灾害下输电系统的弹性评估方法及其提升措施[J].电力工程技术, 2024, 43(2):239-247.
[4] 苗利明, 邱树伟, 张杰洵, 等.基于嵌入式的古建筑火灾超早期检测系统的实现[J].软件工程, 2023, 26(1):59-62.
[5] YANG Songxi, HUANG Qunying, YU Manzhu.Advancements in remote sensing for active fire detection: a review of datasets and methods[J].Science of The Total Environment, 2024, 943: 173273.
[6] 沈龙, 钱国超, 彭兆裕, 等.绝缘子污秽等级的高光谱特征优化识别技术研究[J].电力工程技术, 2022, 41(2):156-162.
[7] XIA Jingjing, ZHOU Yi, ZENG Jin.Dual-wavelength optical sensor for fire detection and measurement of aerosol mass concentration[J].Fire Safety Journal, 2024, 146: 104129.
[8] 马庆禄, 马恋, 孔国英, 等.基于红外热成像的隧道火焰检测技术研究[J].火灾科学, 2022, 31(4):244-251.
[9] YANDOUZI M, BERRAHAL M, GRARI M, et al.Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones[J].Bulletin of Electrical Engineering and Informatics, 2024, 13(4): 2784-2796.
[10] 安金铭, 梁秀满, 王雁, 等.Real-ESRGAN网络与形态学结合的烧结断面火焰图像复原方法[J].华北理工大学学报(自然科学版), 2023, 45(2):60-68.
[11] 赵振强, 何水原, 梁永志.基于Faster R-CNN的遥感影像船舶检测识别[J].测绘通报, 2021(11):59-64.
[12] ZHANG Haoran, YIN Zhenyu, ZHANG Ning, et al.A scale-adaptive mask R-CNN strategy for foreground particle segmentation and geometrical analysis of granular aggregates[J].Applied Soft Computing, 2024, 164: 111931.
[13] SHAMSOSHOARA A, AFGHAH F, RAZI A, et al.Aerial imagery pile burn detection using deep learning: the FLAME dataset[J].Computer Networks, 2021, 193: 108001.
[14] 荆启文, 郝思鹏, 李思源.基于改进YOLOv8算法的10kV配电线路导线裸露检测[J].电力工程技术, 2025, 44(3):201-210.
[15] WANG C, HE W, NIE Y, et al.Gold-YOLO: efficient object detector via gather-and-distribute mechanism[EB/OL].[2025-03-01].https://doi.org/10.48550/arXiv.2309.11331.
[16] 陈飞宇, 张应迁, 吴嘉懿, 等.基于GOLD-YOLO改进YOLOv5模型道路病害检测研究[J].现代计算机, 2024, 30(19):7-12.
[17] AZAD R, AGHDAM E K, RAULAND A, et al.Medical image segmentation review: the success of U-Net[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10076-10095.
[18] 王靖超, 刘勇宏, 杨怡琳, 等.基于YOLOv5s的遥感卫星图像森林火灾检测算法[J].电脑知识与技术, 2023, 19(23):8-12.
[19] TIAN Y, YE Q, DOERMANN D.YOLOv12: attention-centric real-time object detectors[EB/OL].[2025-03-01].https://doi.org/10.48550/arXiv.2502.12524.
[20] WANG Ming, YUE Peng, JIANG Liangcun, et al.An open flame and smoke detection dataset for deep learning in remote sensing based fire detection[J].Geo-spatial Information Science, 2025, 28(2): 511-526.
[21] 赵晨萌, 张卡, 盛业华, 等.多场景烟雾火焰目标检测数据集(S-Firedata)[EB/OL].[2025-03-01].http://geodata.nnu.edu.cn.
[22] RAVI N, GABEUR V, HU Y T, et al.Sam2: segment anything in images and videos[EB/OL].[2025-03-01].https://doi.org/10.48550/arXiv.2408.00714.
[23] WAN Dahang, LU Rongsheng, SHEN Siyuan, et al.Mixed local channel attention for object detection[J].Engineering Applications of Artificial Intelligence, 2023, 123: 106442.
[24] 陈腾杰, 李永安, 张之好, 等.基于改进YOLOv8n+DeepSORT的带式输送机异物检测及计数方法[J].工矿自动化, 2024, 50(8):91-98.
[25] GAO Xuliang, SUN Yubin, XIAO Yao, et al.Adaptive random down-sampling data augmentation and area attention pooling for low resolution face recognition[J].Expert Systems with Applications, 2022, 209: 118275.
[26] 冯金秋, 燕芳, 杨阳, 等.MMF-YOLO晶圆模具表面微缺陷检测算法[J].计算机工程与应用, 2025, 61(15):132-143.
[27] ZHENG Zhaohui, WANG Ping, LIU Wei, et al.Distance-IoU loss: faster and better learning for bounding box regression[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12993-13000.
[28] ZHANG H, ZHANG S.Focaler-IoU: more focused intersection over union loss[EB/OL].[2025-03-01].https://doi.org/10.48550/arXiv.2401.10525.
[29] LIU Can, WANG Kaige, LI Qing, et al.Powerful-IoU: more straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism[J].Neural Networks, 2024, 170: 276-284.
[30] 项家灏, 徐慧英, 徐广平, 等.MECW-YOLO:基于改进YOLOv8的无人机视角小目标检测算法[J/OL].计算机工程与科学, 1-12[2025-08-29].https://link.cnki.net/urlid/43.1258.TP.20241225.1106.008.
[31] WANG Gang, CHEN Yanfei, AN Pei, et al.UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios[J].Sensors, 2023, 23(16): 7190.
[32] 曹继卫, 罗飞, 丁炜超.BS-YOLO:基于BSAM注意力机制和SCConv的小目标检测算法[J/OL].计算机工程, 1-10[2025-08-29].https://doi.org/10.19678/j.issn.1000-3428.0070159.
[33] KHANAM R, HUSSAIN M.YOLOv11: an overview of the key architectural enhancements[EB/OL].[2025-03-01].https://doi.org/10.48550/arXiv.2410.17725.
[34] ZHAO Chenmeng, ZHAO Like, ZHANG Ka, et al.Smoke and fire-you only look once: a lightweight deep learning model for video smoke and flame detection in natural scenes[J].Fire, 2025, 8(3): 104.

面向无人机与遥感影像的烟雾火焰目标提取方法

The method of smoke and flame target extraction facing UAV and remote sensing images

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈楠, 张标, 杨楠, 刘洲洲. 双层耦合非参数Bayesian的遥感图像时空反射率融合[J]. 测绘通报, 2025, 0(9): 45-50.
[2]	唐芝青, 张涛, 王培玉, 向导, 刘海飞, 刘仁峰, 贺江江. ADM-YOLOv11:动态自适应铁塔视频多尺度目标检测算法[J]. 测绘通报, 2025, 0(9): 70-77.
[3]	张辰, 储云志, 吴兆福, 徐立晨, 黄建伟, 李水平. 复杂光照下YOLOv7深度学习算法驱动的标志点区域提取[J]. 测绘通报, 2025, 0(9): 135-139.
[4]	王英谋, 李雷. 基于遥感影像综合生产平台智能协同的DOM生产技术[J]. 测绘通报, 2025, 0(8): 142-148.
[5]	王立志, 肖东升. IVYA-SIAM联合优化的多模态人口空间化模型构建及驱动效应分析[J]. 测绘通报, 2025, 0(8): 95-99,106.
[6]	殷俊婕, 关戴婉静, 李浩, 张晓阳, 马于杰, 邢汉发. 面向海岸线排污口的无人机目标识别数据集[J]. 测绘通报, 2025, 0(8): 112-117.
[7]	董斯源, 杨元维, 高贤君, 谭美淋, 杜斌, 屈伟军, 陈宁生. 基于贪心算法与空间关系自优化影像数据集高效筛选方法[J]. 测绘通报, 2025, 0(8): 100-106.
[8]	魏文豪, 衷诚, 郭健辰, 张正林, 连静. 基于改进YOLOv11的光伏场区施工进度检测[J]. 测绘通报, 2025, 0(7): 132-137,163.
[9]	孙基源, 纪松, 高定, 李凯, 张芮莹. SFR-YOLO:改进YOLOv8的无人机图像小目标检测算法[J]. 测绘通报, 2025, 0(7): 32-39.
[10]	杜建超, 张雷, 白晋颖, 李婷婷. 基于PIE-Engine的陕西省公路网遥感影像分析[J]. 测绘通报, 2025, 0(7): 46-51.
[11]	涂梨平, 陈美球, 冷鹏. 基于目标检测算法YOLOv 9的滑坡隐患识别——以永新县为例[J]. 测绘通报, 2025, 0(6): 37-42,102.
[12]	赵辰瑞, 连增增, 田亚林, 贺刘辉, 陈浩, 王鹏辉, 王孟奇. BiLSTM-Chan算法在超宽带室内定位中的应用[J]. 测绘通报, 2025, 0(5): 100-105.
[13]	宋维凯, 柯福阳, 魏民, 黄钰洲, 朱尚峻. 非接触式单目视觉高精度自动沉降监测[J]. 测绘通报, 2025, 0(5): 145-151.
[14]	徐东升, 张瑞, 席瑞杰. 基于图像处理的铁轨伤损检测[J]. 测绘通报, 2025, 0(5): 125-130,137.
[15]	李娟, 蔡哲理, 向娟, 袁方芳, 袁光碧, 雷邦俊. 基于中高分辨率遥感影像的植被综合盖度时空演变分析[J]. 测绘通报, 2025, 0(5): 21-26.