文献类型：期刊文献

中文题名：基于改进YOLOv5的红树林单木目标检测研究

英文题名：Object Detection of Individual Mangrove Based on Improved YOLOv5

作者：马永康[1,2] 刘华[1,2] 凌成星[1,2] 赵峰[1,2] 姜怡[1,2] 张雨桐[1,2]

第一作者：马永康

通信作者：Liu, H[1];Liu, H[2]

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2022

卷号：59

期号：18

起止页码：426-436

中文期刊名：激光与光电子学进展

外文期刊名：Laser & Optoelectronics Progress

收录：CSTPCD;;Scopus;WOS:【ESCI(收录号:WOS:000892527800049)】；北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：高分湿地资源监测应用子系统(二期)(21-Y30B02-9001-19/22-2)。

语种：中文

中文关键词：红树林单木;无人机影像;深度学习;目标检测;YOLOv5-ECA

外文关键词：individual mangrove;drone image;deep learning;object detection;YOLOv5-ECA

分类号：S757.

摘要：针对无人机影像中红树林单木目标较小且分布密集,对其检测时自动化程度不高、效率低等问题,基于深度学习方法提出了一种红树林单木目标检测模型(YOLOv5-ECA),以实现对无人机影像中红树林单木快速、精确的自动识别和定位。首先利用开源软件LabelImg在选取的无人机影像上依次标注目标树,构建红树林单木数据集;其次选择YOLOv5为基础目标检测模型,依据目标分布密集且尺寸较小的特点对其进行优化和改进;使用有效通道注意力(ECA)机制对CSPDarknet53骨干网络进行改进,避免降维的同时增强特征表达能力,并在SPP模块引入SoftPool改进池化操作,保留更多细节特征信息;最后利用ACON自适应激活函数自适应地决定是否激活神经元。结果表明:使用已构建的数据集对改进前后的网络进行训练,在测试集上对比准确率、召回率、平均精准度的均值(mAP)@0.5等参数,各模型略有差异但均趋于收敛;所提YOLOv5-ECA的平均检测精度较YOLOv5提高了3.2个百分点,较YOLOv4提升了5.19个百分点,同时训练损失也更低,能够快速、精准且自动化地检测红树林单木目标,较好地提升了对红树林单木的识别和定位能力。
In this study,an individual mangrove object detection model called YOLOv5-ECA based on deep learning is proposed to automatically identify and locate individual mangroves with high accuracy aiming at the challenges of small and dense individual mangroves in drone images,resulting in low automation and efficiency for detecting them.First,the opensource software LabelImg is used to mark the target tree on the selected drone image,which is applied to construct the individual mangrove dataset.Then,the YOLOv5 is used as the basic object detection model to maximize and enhance the target tree,and achieving this is based on the characteristics of dense distribution and small size of objects.The efficient channel attention(ECA)mechanism enhances the CSPDarknet53 backbone network to avoid dimensionality reduction while enhancing feature expression capabilities.Furthermore,the enhanced SoftPool pooling operation is introduced into the SPP module to retain more detailed feature information.Finally,the ACON adaptive activation function determines whether the neuron is activated.The results demonstrate that the constructed dataset is used to train the network before and after improvement,and the accuracy,recall,and mean average precision(mAP)@0.5 parameters are compared.The results of different models are slightly different,but they all tend to converge.The proposed YOLOv5-ECA’s average detection accuracy is 3.2 percentage points higher than YOLOv5 and 5.19 percentage points higher than YOLOv4,and its training loss is also lower.The deep learningbased YOLOv5-ECA model can quickly,accurately,and automatically detect individual mangroves and significantly enhance the ability to identify and locate them.