面向海岸线排污口的无人机目标识别数据集

doi:10.13474/j.cnki.11-2246.2025.0818

测绘通报 ›› 2025, Vol. 0 ›› Issue (8): 112-117.doi: 10.13474/j.cnki.11-2246.2025.0818

面向海岸线排污口的无人机目标识别数据集

殷俊婕¹, 关戴婉静², 李浩², 张晓阳³, 马于杰⁴, 邢汉发^1,4

1. 华南师范大学北斗研究院, 广东佛山 528000;
2. 广东省海洋发展规划研究中心, 广东广州 510000;
3. 广州市阿尔法软件信息技术有限公司, 广东广州 510000;
4. 华南师范大学地理科学学院, 广东广州 510000

收稿日期:2025-01-13 出版日期:2025-08-25 发布日期:2025-09-02
通讯作者: 关戴婉静。E-mail:guandaiwanjing@hotmail.com E-mail:guandaiwanjing@hotmail.com
作者简介:殷俊婕(2002—),女,硕士生,主要研究方向为遥感图像处理。E-mail:2024025490@m.scnu.edu.cn
基金资助:
国家自然科学基金面上项目(42271470)

UAV-based object recognition dataset for coastal sewage outfalls

YIN Junjie¹, GUAN Daiwanjing², LI Hao², ZHANG Xiaoyang³, MA Yujie⁴, XING Hanfa^1,4

1. BeiDou Research Institute, South China Normal University, Foshan 528000, China;
2. Guangdong Provincial Marine Development Planning Research Center, Guangzhou 510000, China;
3. Guangzhou Alpha Software Information Technology Co., Ltd., Guangzhou 510000, China;
4. School of Geography, South China Normal University, Guangzhou 510000, China

Received:2025-01-13 Online:2025-08-25 Published:2025-09-02

摘要/Abstract

摘要： 海岸线排污口识别是实现海域监管的重要内容,能够为海域的生态和资源安全提供重要保障措施。针对当前无人机视角下海岸线排污口检测领域存在的专用数据集匮乏及目标识别算法精度不足等问题,本文构建了高质量的海岸线排污口数据集,并提出了一种基于改进YOLOv8n模型的检测方法。首先,以广东省阳江市海岸区域为研究区,通过无人机多高度采集影像,构建了涵盖多样排污口特征的数据集;然后,在YOLOv8n模型中引入SimAM无参数注意力机制,优化特征提取与融合,并融合NWD与CIoU损失函数,解决了边界模糊与目标重叠问题。试验验结果表明,改进模型在精度、召回率及mAP等指标上均优于原始模型,mAP达98.27%。本文为海岸线排污口监测提供了智能化解决方案,为海域监管与污染治理提供了技术支撑。

关键词: 海岸线, 目标检测, YOLOv8n, SimAM注意力机制, 无人机技术

Abstract: The identification of coastal sewage outfalls is a crucial aspect of marine supervision,providing essential safeguards for the ecological and resource security of marine areas.Addressing the current challenges of insufficient specialized datasets and the lack of precision in target recognition algorithms for coastal sewage outfall detection using unmanned aerial vehicle (UAV)imagery,this study constructs a high-quality dataset of coastal sewage outfalls and proposes an enhanced detection method based on the improved YOLOv8n model.Initially,focusing on the coastal region of Yangjiang city,Guangdong province,the study employs UAVs to capture images at various altitudes,establishing a comprehensive dataset that encompasses diverse characteristics of sewage outfalls.Subsequently,the YOLOv8n model is augmented with the SimAM parameter-free attention mechanism to refine feature extraction and fusion,alongside the integration of NWD and CIoU loss functions to address issues of boundary ambiguity and target overlap.Experimental results demonstrate that the enhanced model surpasses the original in terms of precision,recall rate,and mAP,achieving an mAP of 98.27%.This research offers an intelligent solution for monitoring coastal sewage outfalls,contributing technological support for marine supervision and pollution control.

Key words: coastal, target detection, YOLOv8n, SimAM attention mechanism, UAV technology

中图分类号:

P237
P229

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.

YIN Junjie, GUAN Daiwanjing, LI Hao, ZHANG Xiaoyang, MA Yujie, XING Hanfa. UAV-based object recognition dataset for coastal sewage outfalls[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 112-117.

参考文献

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面向海岸线排污口的无人机目标识别数据集

UAV-based object recognition dataset for coastal sewage outfalls

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