在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第...在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第7层卷积模块和颈部网络卷积模块替换成动态蛇形卷积(DSConv),提高算法的特征提取性能,并强化算法对微小特征的学习能力;然后在颈部与检测头之间添加多维协作注意力机制(MCA),加强颈部特征融合,增强算法对小目标的检测能力,并抑制无关背景信息;最后使用SIoU损失函数替换原YOLOv8中的CIoU损失函数,加快算法的收敛速度和回归精度。实验结果表明,MDSYOLOv8在公开数据集KMU上对烟雾目标的检测精度mAP达到95.89%,相较于基线YOLOv8提高了3.33个百分点,具有卓越的检测性能。此外,本研究采集互联网上的无人机航拍火灾图像制作UFF(UAV field fire)数据集,主要对象为火焰和烟雾,包含森林和城市等火灾隐患可能发生场景。在自制数据集UFF上进行深度实验分析,MDSYOLOv8的检测精度达到93.98%,检测速度为54帧/s,并且能同时识别烟雾和火焰两种火灾场景中的主要目标,与主流目标检测方法相比,在检测精度和效率方面均展现出明显优势,更加契合航拍场景下的火灾检测应用。展开更多
Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and...Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.展开更多
基于深度学习算法的建筑垃圾分类检测技术对建筑垃圾回收和资源再利用具有重要意义。提出了改进的YOLOv7算法实现对建筑垃圾的分类检测。改进算法采用内容感知特征重组(content-aware reassembly of features,CARAFE)上采样算子替换YOL...基于深度学习算法的建筑垃圾分类检测技术对建筑垃圾回收和资源再利用具有重要意义。提出了改进的YOLOv7算法实现对建筑垃圾的分类检测。改进算法采用内容感知特征重组(content-aware reassembly of features,CARAFE)上采样算子替换YOLOv7中最邻近插值方式的上采样算子,从而提高了目标检测精度;引入分布移位卷积(distribution shifting convolution,DSConv)模块替换YOLOv7的头部网络中部分传统卷积,实现了模型的轻量化。结果表明,改进算法的m AP值达到了90.7%,模型计算量仅为96G。该方法具有准确率高、稳健性强等特点,在建筑垃圾分类检测实际场景中具有较高的应用价值。展开更多
为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting...为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting convolution,DSConv)来替换YOLOv7-tiny网络中的3×3卷积,以提高模型的计算速度并降低计算复杂度;在模型的检测头部分,添加了高效解耦头结构,以提高模型的准确度和稳定性;并采用明智的交并比(wise intersection over union,WIoU)损失函数来提高正样本的权重,使模型更加关注缺销螺栓目标,以减少正负样本不平衡带来的噪声干扰。实验结果表明,改进YOLOv7-tiny算法对输电线路螺栓缺销检测的平均精度均值达到90.6%,检测速度达到143.0帧/s,同时实现了检测的高速度和高精度。该算法在无人机输电线路巡检中具有一定的优势。展开更多
文摘在林业管理中,及时发现火灾并识别其规模对于安全防护和治理火灾至关重要。针对现有火灾检测算法存在的精度低、漏检误检和实时性不足等问题,提出一种无人机航拍图像下火灾实时检测算法——MDSYOLOv8。以YOLOv8为基线算法,将骨干网络第7层卷积模块和颈部网络卷积模块替换成动态蛇形卷积(DSConv),提高算法的特征提取性能,并强化算法对微小特征的学习能力;然后在颈部与检测头之间添加多维协作注意力机制(MCA),加强颈部特征融合,增强算法对小目标的检测能力,并抑制无关背景信息;最后使用SIoU损失函数替换原YOLOv8中的CIoU损失函数,加快算法的收敛速度和回归精度。实验结果表明,MDSYOLOv8在公开数据集KMU上对烟雾目标的检测精度mAP达到95.89%,相较于基线YOLOv8提高了3.33个百分点,具有卓越的检测性能。此外,本研究采集互联网上的无人机航拍火灾图像制作UFF(UAV field fire)数据集,主要对象为火焰和烟雾,包含森林和城市等火灾隐患可能发生场景。在自制数据集UFF上进行深度实验分析,MDSYOLOv8的检测精度达到93.98%,检测速度为54帧/s,并且能同时识别烟雾和火焰两种火灾场景中的主要目标,与主流目标检测方法相比,在检测精度和效率方面均展现出明显优势,更加契合航拍场景下的火灾检测应用。
基金funded by Jilin Province Innovation and Entrepreneurship Talent Project,grant number 2023QN15funded by Science and Technology Development Plan Project of Jilin Province,grant number 20220202035NC.
文摘Rice spike detection and counting play a crucial role in rice yield research.Automatic detection technology based on Unmanned Aerial Vehicle(UAV)imagery has the advantages of flexibility,efficiency,low cost,safety,and reliability.However,due to the complex field environment and the small target morphology of some rice spikes,the accuracy of detection and counting is relatively low,and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results.To solve the above problems,this paper improves the You Only Look Once v8(YOLOv8)model,proposes a new method for detecting and counting rice spikes,and designs a comparison experiment using rice spike detection in different periods.Themethod improves the model’s ability to detect rice ears with special morphologies by introducing a Dynamic Snake Convolution(DSConv)module into the Bottleneck of the C2f structure of YOLOv8,which enhances themodule’s ability to extract elongated structural features;In addition,the Weighted Interpolation of Sequential Evidence for Intersection over Union(Wise-IoU)loss function is improved to reduce the harmful gradient of lowquality target frames and enhance themodel’s ability to locate small spikelet targets,thus improving the overall detection performance of the model.The experimental results show that the enhanced rice spike detection model has an average accuracy of 91.4%and a precision of 93.3%,respectively,which are 2.3 percentage points and 2.5 percentage points higher than those of the baseline model.Furthermore,it effectively reduces the occurrence of missed and false detections of rice spikes.In addition,six rice spike detection models were developed by training the proposed models with images of rice spikes at themilk and waxmaturity stages.The experimental findings demonstrated that the models trained on milk maturity data attained the highest detection accuracy for the same data,with an average accuracy of 96.2%,an R squared(R^(2))value of 0.71,and a Rootmean squared error(RMSE)of 20.980.This study provides technical support for early and non-destructive yield estimation in rice in the future.
文摘为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting convolution,DSConv)来替换YOLOv7-tiny网络中的3×3卷积,以提高模型的计算速度并降低计算复杂度;在模型的检测头部分,添加了高效解耦头结构,以提高模型的准确度和稳定性;并采用明智的交并比(wise intersection over union,WIoU)损失函数来提高正样本的权重,使模型更加关注缺销螺栓目标,以减少正负样本不平衡带来的噪声干扰。实验结果表明,改进YOLOv7-tiny算法对输电线路螺栓缺销检测的平均精度均值达到90.6%,检测速度达到143.0帧/s,同时实现了检测的高速度和高精度。该算法在无人机输电线路巡检中具有一定的优势。
