水中细菌数量的多少作为衡量水质的重要指标之一,其变化可以间接地反映水体污染程度。水中细菌总数反映了水体被有机物污染的程度。为了能快速、高效、准确地统计出细菌总量,将深度学习引入环境工程中,提出一种基于YOLOv5的细菌计数改...水中细菌数量的多少作为衡量水质的重要指标之一,其变化可以间接地反映水体污染程度。水中细菌总数反映了水体被有机物污染的程度。为了能快速、高效、准确地统计出细菌总量,将深度学习引入环境工程中,提出一种基于YOLOv5的细菌计数改进算法。首先对自建细菌数据集使用K-means++聚类算法获得和特征图更加匹配的先验框,之后,在网络中增加一层小目标检测层,提高模型对图像中小目标的敏感度,最后,在骨干网络中C3层后引入一种协调注意力(CA),其不仅能捕获跨通道信息,还能捕获方向感知和位置敏感信息,提高对小目标的识别度,这有助于模型提高对密集预测任务的性能。实验表明,相比于传统的YOLOv5框架算法,改进后的算法在测试集上的平均检测率到达93.04%,提高了7.68%,同时训练损失也更低,验证了增加小目标检测层和注意力机制对细菌图像这种小目标密集检测有较好效果。该算法的引入可以提高细菌计数效率和计数精准度,同时实现对细菌数量的高精度分析,从而进一步深入研究微生物群落的结构、环境污染的程度以及疾病的诊断与治疗等方面,为环境监测提供了有力支持。The number of bacteria in water is one of the important indicators to measure water quality, and the change of bacteria number can indirectly reflect the degree of water pollution. At the same time, the total number of bacteria in water reflects the degree of pollution by organic matter. In order to count the total amount of bacteria quickly, efficiently and accurately, an improved algorithm of bacteria counting based on YOLOv5 is proposed by introducing deep learning into environmental engineering. Firstly, a K-means++ clustering algorithm is used for the self-built bacteria dataset to obtain priori frames that match more closely with the feature map. Secondly, a small target detection layer is added to the network to improve the sensitivity of the model to small targets in images, finally, a coordinated attention (CA) is introduced after the C3 layer in the backbone network, which can capture not only cross-channel information but also orientation-aware and position-sensitive information to improve the recognition of small targets, which helps the model to improve its performance for dense prediction tasks. Experiments show that the improved algorithm achieves an average detection rate of 93.04% on the test set compared to the traditional YOLOv5 framework algorithm, an improvement of 7.68%, as well as a lower training loss, verifying that the addition of the small target detection layer and the attention mechanism is more effective for dense detection of small targets like bacterial images. The introduction of this algorithm can improve the efficiency and accuracy of bacterial counting, and can achieve high precision analysis of bacterial counts, further deepening the study of the structure of microbial communities, the degree of environmental pollution, and the diagnosis and treatment of diseases, providing strong support for environmental monitoring.展开更多
针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Fo...针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Focal-EIoU(focal embedding intersection over union)损失函数,有效提升了检测模型的性能;其次为增强检测模型对小目标缺陷的检测效果,减少缺陷误检和漏检的概率,将骨干网络中浅层特征图融合到双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)中,增加针对小目标的预测层;最后在YOLO v5l中引入坐标注意力机制(coordinate attention,CA),提高模型对图像中感兴趣区域的敏感程度,减少冗余背景信息的干扰。3种改进对平均检测准确率(mean average precision,mAP)的提升分别为2.0、2.9、5.9个百分点。将三种有效改进融合到一起,检测结果表明:本文提出的YOLO v5l-Im模型的mAP达到了92.1%,较原模型的85.5%提升了6.5个百分点。由此可见,所做的改进有效增强了YOLO v5l对排水管道缺陷的检测能力。展开更多
文摘水中细菌数量的多少作为衡量水质的重要指标之一,其变化可以间接地反映水体污染程度。水中细菌总数反映了水体被有机物污染的程度。为了能快速、高效、准确地统计出细菌总量,将深度学习引入环境工程中,提出一种基于YOLOv5的细菌计数改进算法。首先对自建细菌数据集使用K-means++聚类算法获得和特征图更加匹配的先验框,之后,在网络中增加一层小目标检测层,提高模型对图像中小目标的敏感度,最后,在骨干网络中C3层后引入一种协调注意力(CA),其不仅能捕获跨通道信息,还能捕获方向感知和位置敏感信息,提高对小目标的识别度,这有助于模型提高对密集预测任务的性能。实验表明,相比于传统的YOLOv5框架算法,改进后的算法在测试集上的平均检测率到达93.04%,提高了7.68%,同时训练损失也更低,验证了增加小目标检测层和注意力机制对细菌图像这种小目标密集检测有较好效果。该算法的引入可以提高细菌计数效率和计数精准度,同时实现对细菌数量的高精度分析,从而进一步深入研究微生物群落的结构、环境污染的程度以及疾病的诊断与治疗等方面,为环境监测提供了有力支持。The number of bacteria in water is one of the important indicators to measure water quality, and the change of bacteria number can indirectly reflect the degree of water pollution. At the same time, the total number of bacteria in water reflects the degree of pollution by organic matter. In order to count the total amount of bacteria quickly, efficiently and accurately, an improved algorithm of bacteria counting based on YOLOv5 is proposed by introducing deep learning into environmental engineering. Firstly, a K-means++ clustering algorithm is used for the self-built bacteria dataset to obtain priori frames that match more closely with the feature map. Secondly, a small target detection layer is added to the network to improve the sensitivity of the model to small targets in images, finally, a coordinated attention (CA) is introduced after the C3 layer in the backbone network, which can capture not only cross-channel information but also orientation-aware and position-sensitive information to improve the recognition of small targets, which helps the model to improve its performance for dense prediction tasks. Experiments show that the improved algorithm achieves an average detection rate of 93.04% on the test set compared to the traditional YOLOv5 framework algorithm, an improvement of 7.68%, as well as a lower training loss, verifying that the addition of the small target detection layer and the attention mechanism is more effective for dense detection of small targets like bacterial images. The introduction of this algorithm can improve the efficiency and accuracy of bacterial counting, and can achieve high precision analysis of bacterial counts, further deepening the study of the structure of microbial communities, the degree of environmental pollution, and the diagnosis and treatment of diseases, providing strong support for environmental monitoring.
文摘针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Focal-EIoU(focal embedding intersection over union)损失函数,有效提升了检测模型的性能;其次为增强检测模型对小目标缺陷的检测效果,减少缺陷误检和漏检的概率,将骨干网络中浅层特征图融合到双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)中,增加针对小目标的预测层;最后在YOLO v5l中引入坐标注意力机制(coordinate attention,CA),提高模型对图像中感兴趣区域的敏感程度,减少冗余背景信息的干扰。3种改进对平均检测准确率(mean average precision,mAP)的提升分别为2.0、2.9、5.9个百分点。将三种有效改进融合到一起,检测结果表明:本文提出的YOLO v5l-Im模型的mAP达到了92.1%,较原模型的85.5%提升了6.5个百分点。由此可见,所做的改进有效增强了YOLO v5l对排水管道缺陷的检测能力。
文摘基于深度学习的目标检测算法直接应用于航天光学遥感(Space Optical Remote Sensing,SORS)复杂场景图像中会出现舰船目标检测效果不佳的问题。针对该问题,本文以近海复杂背景的密集排布舰船和远海多干扰中小目标舰船为检测对象,提出一种改进的YOLOX-s(Improved You Only Look Once-s,IM-YOLO-s)算法。在特征提取阶段,引入CA位置注意力模块,分别从高度与宽度两个方向对目标信息的位置进行权重分配,提高了模型的检测精度;在特征融合阶段,将BiFPN加权特征融合算法应用到IM-YOLO-s的颈部结构,进一步提升了小目标船只检测精度;在模型优化训练阶段,以CIoU损失替代IoU损失、以变焦损失替代置信度损失、调整类别损失权重,增大了正样本分布密集区域的训练权重,减少了密集分布船只的漏检率。另外,在HRSC2016数据集的基础上增加额外的离岸中小舰船图像,自建了HRSC2016-Gg数据集,HRSC2016-Gg数据集增强了海上船只及中小像素船只检测时的鲁棒性。通过数据集HRSC2016-Gg评测算法性能,实验结果表明:IM-YOLO-s对于SORS场景舰船检测的召回率为97.18%,AP@0.5为96.77%,F1值为0.95,较原YOLOX-s算法分别提高了2.23%,2.40%和0.01。这充分表明该算法可以对SORS复杂背景图像进行高质量舰船检测。
