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
目前随着人工智能的快速发展,机器学习已经广泛应用于各个领域,并对实验中获得的小数据进行模拟分析。本研究以微藻为基础,利用收集的9组文献中对微藻的研究结果为数据,使用四种算法,即BP神经网络、支持向量机、随机森林和径向基函数神...目前随着人工智能的快速发展,机器学习已经广泛应用于各个领域,并对实验中获得的小数据进行模拟分析。本研究以微藻为基础,利用收集的9组文献中对微藻的研究结果为数据,使用四种算法,即BP神经网络、支持向量机、随机森林和径向基函数神经网络,使用MATLAB软件进行建模和分析,与原文献中的响应面分析法进行对比,得出以下主要结论:通过比较相对系数(R2)、平均绝对误差(MAE)、平均偏差误差(MBE)、均方根误差(RMSE)和均方误差(MSE),相比于传统的响应面分析法,机器学习算法表现出更好的预测效果,其中随机森林和径向基函数神经网络的相对系数最接近于1,预测效果最好,其次是BP神经网络和支持向量机。Currently, with the rapid development of artificial intelligence, machine learning has been widely applied in various fields and used to simulate and analyze small data obtained from experiments. This study based on microalgae and using the collected 9 groups of microalgae research results in the literature for the data, using four kinds of algorithms, namely, the BP neural network, support vector machine (SVM), random forests (RF) and radial basis function (RBF) neural network, using MATLAB software for modeling and analysis, comparing with the original documents by response surface analysis method. The following main conclusions: by comparing the relative coefficient (R2), the mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE) and mean square error (MSE), compared with the traditional response surface analysis method, machine learning algorithms show better prediction effect, random forests and the relative coefficient of radial basis function (RBF) neural network is the most close to 1, the prediction effect is best, followed by BP neural network and support vector machine.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.
文摘目前随着人工智能的快速发展,机器学习已经广泛应用于各个领域,并对实验中获得的小数据进行模拟分析。本研究以微藻为基础,利用收集的9组文献中对微藻的研究结果为数据,使用四种算法,即BP神经网络、支持向量机、随机森林和径向基函数神经网络,使用MATLAB软件进行建模和分析,与原文献中的响应面分析法进行对比,得出以下主要结论:通过比较相对系数(R2)、平均绝对误差(MAE)、平均偏差误差(MBE)、均方根误差(RMSE)和均方误差(MSE),相比于传统的响应面分析法,机器学习算法表现出更好的预测效果,其中随机森林和径向基函数神经网络的相对系数最接近于1,预测效果最好,其次是BP神经网络和支持向量机。Currently, with the rapid development of artificial intelligence, machine learning has been widely applied in various fields and used to simulate and analyze small data obtained from experiments. This study based on microalgae and using the collected 9 groups of microalgae research results in the literature for the data, using four kinds of algorithms, namely, the BP neural network, support vector machine (SVM), random forests (RF) and radial basis function (RBF) neural network, using MATLAB software for modeling and analysis, comparing with the original documents by response surface analysis method. The following main conclusions: by comparing the relative coefficient (R2), the mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE) and mean square error (MSE), compared with the traditional response surface analysis method, machine learning algorithms show better prediction effect, random forests and the relative coefficient of radial basis function (RBF) neural network is the most close to 1, the prediction effect is best, followed by BP neural network and support vector machine.
