CN110866561A - Plastic bottle color sorting method based on image recognition - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及图像处理的技术领域,具体涉及一种基于图像识别的塑料瓶颜色分选方法。The invention relates to the technical field of image processing, in particular to a color sorting method for plastic bottles based on image recognition.
背景技术Background technique
随着时代的发展和人民生活水平的提高,饮料的生产和消费量不断增长。对于塑料瓶的处理,现行的方法大多数是掩埋和燃烧,由于塑料瓶自然降解的时间特别长,同时降解过程中还会产生一些有害物质;燃烧法会产生大量有害气体等对环境造成严重的污染现象,也会危害人民的健康,同时还会造成资源的浪费。因此国家实行对塑料瓶进行回收利用,为了保证回收材料的品质,对塑料瓶进行颜色分类。目前我国对塑料瓶进行颜色分类主要靠人工分选,且效率不高,是一项耗费人力时间和成本的工作。利用深度学习方法极大地提高了对塑料瓶颜色检测和分类的工作效率,同时也节约了大量的劳动力和成本。With the development of the times and the improvement of people's living standards, the production and consumption of beverages continue to grow. For the disposal of plastic bottles, most of the current methods are burial and burning. Because the natural degradation time of plastic bottles is very long, and some harmful substances will be produced during the degradation process; the burning method will produce a large amount of harmful gases and cause serious environmental damage Pollution will also endanger people's health and cause waste of resources. Therefore, the country implements the recycling of plastic bottles. In order to ensure the quality of recycled materials, plastic bottles are classified by color. At present, the color classification of plastic bottles in my country mainly relies on manual sorting, and the efficiency is not high. It is a labor-intensive, time-consuming and cost-intensive work. The use of deep learning methods has greatly improved the work efficiency of color detection and classification of plastic bottles, and also saved a lot of labor and costs.
目前颜色识别方法有:通过边缘检测和图像二值化处理,对图像进行分割等操作,利用分割后其他区域上的颜色与基准区域的颜色的色差关系来确定颜色分类;通过权重分块,对RGB彩色图像进行HSV颜色空间转换,根据H,V值进行颜色分类。现有方法不适合数据集较大的实验,且分类速度和准确性不高。The current color recognition methods include: segmenting the image through edge detection and image binarization, and using the color difference relationship between the color in other areas after segmentation and the color in the reference area to determine the color classification; The RGB color image is converted into HSV color space, and the color is classified according to the H and V values. Existing methods are not suitable for experiments with large datasets, and the classification speed and accuracy are not high.
发明内容SUMMARY OF THE INVENTION
本发明提供一种准确率高、快速性好、实用性强的塑料瓶颜色分选方法,适用于不同的环境下塑料瓶颜色的变化。The invention provides a plastic bottle color sorting method with high accuracy, good rapidity and strong practicability, which is suitable for changing the color of plastic bottles in different environments.
一种基于图像识别的塑料瓶颜色分选方法,包括:A color sorting method for plastic bottles based on image recognition, comprising:
S1,采集塑料瓶图像,对图像进行去噪、均值化、去雾以及裁剪操作后,将图像按比例划分为训练图像数据集、验证图像数据集和待测图像数据集;S1, collect an image of a plastic bottle, perform denoising, averaging, dehazing and cropping operations on the image, and divide the image into a training image data set, a verification image data set and an image data set to be tested in proportion;
S2,建立塑料瓶颜色分选的深度学习网络模型,将训练图像数据集输入网络模型进行训练,确定深度学习网络模型及模型参数;S2, establish a deep learning network model for color sorting of plastic bottles, input the training image data set into the network model for training, and determine the deep learning network model and model parameters;
S3,将验证图像数据集输入已确定的深度学习网络模型中,检验深度学习网络模型;S3, input the verification image data set into the determined deep learning network model to test the deep learning network model;
S4,将待测图像数据集输入到深度学习网络模型中,得到待测图像数据集中塑料瓶颜色分类结果。S4, the image data set to be tested is input into the deep learning network model, and the color classification result of the plastic bottle in the image data set to be tested is obtained.
进一步,所述S1中包括以下步骤:Further, the S1 includes the following steps:
S11,利用工业相机对不同颜色的塑料瓶进行图像采集,形成第一数据集;S11, using an industrial camera to collect images of plastic bottles of different colors to form a first data set;
S12,对第一数据集中的图像进行高斯滤波去噪;S12, performing Gaussian filtering and denoising on the images in the first data set;
S13,将S12处理后的图像进行灰度均值化、去雾操作处理;S13, performing grayscale averaging and dehazing operations on the image processed in S12;
S14,将S13处理后的图形按224*224大小进行裁剪,将裁剪后的图像存入第一数据集,替换原来的图像,形成第二数据集;S14, the graphics processed in S13 are cropped according to the size of 224*224, and the cropped image is stored in the first data set, and the original image is replaced to form a second data set;
S15,对S14中第二数据集的图像将塑料瓶按红色、绿色、蓝色、紫色、黄色、黑色、白色和透明色分类,打上标签,进行不同方向的旋转、镜像、增加对比度等操作后,将第二数据集按照8:1:1的比例进行划分,得到预处理后的训练图像数据集、验证图像数据集和待测图像数据集。S15, classify the plastic bottles according to red, green, blue, purple, yellow, black, white and transparent colors for the images of the second data set in S14, label them, and perform operations such as rotating, mirroring, and increasing contrast in different directions. , and divide the second data set according to the ratio of 8:1:1 to obtain a preprocessed training image data set, a verification image data set and an image data set to be tested.
进一步,所述S2中包括以下步骤:Further, the following steps are included in the S2:
S21,确定输入图像的卷积层和池化层;S21, determine the convolution layer and the pooling layer of the input image;
S22,通过残差网络来实现塑料瓶颜色的特征提取;S22, the feature extraction of the color of the plastic bottle is realized through the residual network;
S23,将提取的特征图输入到全局平均池化层,再将池化特征输入到全连接层进行塑料瓶颜色分类;S23, input the extracted feature map to the global average pooling layer, and then input the pooled features to the fully connected layer for plastic bottle color classification;
S24,设置训练算法中的参数,包括学习率、训练次数和迭代次数;S24, set the parameters in the training algorithm, including the learning rate, the number of training times and the number of iterations;
S25,将训练图像数据集输入建立的网络模型,确定深度学习网络模型及模型参数。S25, input the training image data set into the established network model, and determine the deep learning network model and model parameters.
进一步,所述S21中的卷积层包括第一卷积层和第二卷积层,所述第一卷积层中卷积核的大小为3*3,步长为2;所述第二卷积层中卷积核的大小为3*3,步长为1。Further, the convolution layer in S21 includes a first convolution layer and a second convolution layer, the size of the convolution kernel in the first convolution layer is 3*3, and the stride is 2; the second convolution layer The size of the convolution kernel in the convolutional layer is 3*3, and the stride is 1.
进一步,所述S22中所述的残差网络设置有四个残差块,所述残差块中设置第三层卷积层、第四层卷积层和第五层卷积层。Further, the residual network described in S22 is provided with four residual blocks, and the residual blocks are provided with a third convolutional layer, a fourth convolutional layer and a fifth convolutional layer.
进一步,所述残差块的输出计算公式如下:Further, the output calculation formula of the residual block is as follows:
Y=relu3([relu2(relu1(X,W1),W2)]*W3+X*Wt)Y=relu 3 ([relu 2 (relu 1 (X,W 1 ),W 2 )]*W 3 +X*Wt)
其中,X为输入,Y为输出,W1、W2、W3为各卷积层的权重矩阵,relu1为第三层卷积层输出的激励函数,relu2为第四层卷积层输出的激励函数,relu3为残差块输出的激励函数,Wt为线性变换矩阵。Among them, X is the input, Y is the output, W 1 , W 2 , and W 3 are the weight matrices of each convolutional layer, relu 1 is the excitation function output by the third-layer convolutional layer, and relu 2 is the fourth-layer convolutional layer. The output excitation function, relu 3 is the excitation function output by the residual block, and Wt is the linear transformation matrix.
进一步,第三个卷积层的卷积核为1*1,步长为2,第四个卷积层的卷积核为3*3,步长为1,第五个卷积层的卷积核为1*1,步长为1。Further, the convolution kernel of the third convolutional layer is 1*1, the stride is 2, the convolutional kernel of the fourth convolutional layer is 3*3, the stride is 1, and the volume of the fifth convolutional layer is The product kernel is 1*1, and the step size is 1.
进一步,所述S25中包括以下步骤:Further, the S25 includes the following steps:
S251,判断是否达到设定训练次数,若达到则得出结果,否则重复S251;S251, determine whether the set number of training times is reached, and if so, obtain the result, otherwise repeat S251;
S252,判断是否达到设定迭代次数,若达到则训练结束,否则重复S251;S252, determine whether the set number of iterations is reached, if so, the training ends, otherwise repeat S251;
S253,确定深度学习网络模型及模型参数。S253, determine the deep learning network model and model parameters.
本发明的有益效果是:The beneficial effects of the present invention are:
1、对图像进行高斯滤波去噪和进行灰度均值化、去雾处理,克服了不同光线问题下对图像产生的干扰问题,提高了数据集的质量;1. Perform Gaussian filtering and denoising on the image, grayscale averaging, and dehazing, which overcomes the problem of interference to the image under different lighting conditions and improves the quality of the data set;
2、图像输入用两个3*3卷积核(第一个卷积核步长为2,第二个卷积核步长为1)代替步长为2的7*7卷积核,增加了网络的层数,层与层之间加入激励函数,从而增强了网络的非线性表达能力,同时也减少了参数的数量;2. The image input uses two 3*3 convolution kernels (the first convolution kernel has a stride of 2, and the second convolution kernel has a stride of 1) instead of the 7*7 convolution kernel with a stride of 2, increasing the The number of layers of the network is increased, and the excitation function is added between the layers, thereby enhancing the nonlinear expression ability of the network and reducing the number of parameters;
3、对于残差块,使用先降低网络维度后还原网络维度的方法也减少了计算量。3. For the residual block, the method of first reducing the network dimension and then restoring the network dimension also reduces the amount of computation.
附图说明Description of drawings
图1为本发明的整体流程图;Fig. 1 is the overall flow chart of the present invention;
图2为本发明S1的具体流程图;Fig. 2 is the concrete flow chart of the present invention S1;
图3为本发明S2的具体流程图;Fig. 3 is the concrete flow chart of the present invention S2;
图4为本发明建立的深度学习网络模型结构图。FIG. 4 is a structural diagram of a deep learning network model established by the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整的描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。本领域普通人员在没有做出创造性劳动前提下所获得的所有其他实施例,均属于本发明的保护范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.
参照图1至图4所示,一种基于图像识别的塑料瓶颜色分选方法,包括以下步骤:1 to 4, a color sorting method for plastic bottles based on image recognition includes the following steps:
S1,采集塑料瓶图像,对图像进行去噪、均值化、去雾以及裁剪操作后,将图片按比例划分为训练图像数据集、验证图像数据集和待测图像数据集。具体步骤如下:S1, collect an image of a plastic bottle, perform denoising, averaging, dehazing, and cropping operations on the image, and divide the image into a training image dataset, a validation image dataset, and a test image dataset in proportion. Specific steps are as follows:
S11,利用工业相机对不同颜色的塑料瓶进行图像采集,形成第一数据集;S11, using an industrial camera to collect images of plastic bottles of different colors to form a first data set;
S12,对第一数据集中的图像进行高斯滤波去噪;S12, performing Gaussian filtering and denoising on the images in the first data set;
S13,将S12处理后的图像进行灰度均值化操作,具体方法为:将彩色图像分成三个独立的单通道,然后对每个单通道进行灰度直方图均衡化,再将均衡化后的图像合并为一个通道;再将图像进行去雾操作,使其恢复为彩色图像;S13 , performing a grayscale averaging operation on the image processed in S12. The specific method is as follows: dividing the color image into three independent single channels, then performing grayscale histogram equalization on each single channel, and then equalizing the equalized image The image is merged into one channel; then the image is dehazed to restore it to a color image;
S14,将S13处理后的图形按224*224大小进行剪裁,将剪裁后的图像存入第一数据集,替换原来的图像,形成第二数据集;S14, trim the graph processed in S13 according to the size of 224*224, store the trimmed image in the first data set, replace the original image, and form the second data set;
S15,对S14中第二数据集的图像将塑料瓶按红色、绿色、蓝色、紫色、黄色、黑色、白色和透明色分类,打上标签,进行不同方向的旋转、镜像、增加对比度等操作后,将第二数据集按照8:1:1的比例进行划分,得到预处理后的训练图像数据集、验证图像数据集和待测图像数据集。S15, classify the plastic bottles according to red, green, blue, purple, yellow, black, white and transparent colors for the images of the second data set in S14, label them, and perform operations such as rotating, mirroring, and increasing contrast in different directions. , and divide the second data set according to the ratio of 8:1:1 to obtain a preprocessed training image data set, a verification image data set and an image data set to be tested.
S2,建立塑料瓶颜色分选的深度学习网络模型,将训练图像数据集输入网络模型进行训练,确定深度学习网络模型及模型参数。具体步骤如下:S2, establish a deep learning network model for color sorting of plastic bottles, input the training image data set into the network model for training, and determine the deep learning network model and model parameters. Specific steps are as follows:
S21,确定输入图像的卷积层和池化层。为增强网络非线性表达能力,将卷积层设置为第一卷积层和第二卷积层,所述第一卷积层中卷积核的大小为3*3,步长为2;所述第二卷积层中卷积核的大小为3*3,步长为1。S21, determine the convolution layer and the pooling layer of the input image. In order to enhance the nonlinear expression ability of the network, the convolution layer is set as the first convolution layer and the second convolution layer, the size of the convolution kernel in the first convolution layer is 3*3, and the stride is 2; The size of the convolution kernel in the second convolutional layer is 3*3, and the stride is 1.
S21中图像处理为:输入图像为224*224*3,经过第一层卷积层,卷积核大小为3*3,步长为2,输出64个特征,图像尺寸为112*112;经过第二层卷积层,卷积核大小为3*3,步长为1,输出64个特征,图像尺寸为112*112;再经过步长为2的池化层,输出64个特征,图像尺寸为56*56。The image processing in S21 is: the input image is 224*224*3, after the first convolution layer, the size of the convolution kernel is 3*3, the stride is 2, and 64 features are output, and the image size is 112*112; The second layer of convolutional layer, the convolution kernel size is 3*3, the stride is 1, the output is 64 features, and the image size is 112*112; The size is 56*56.
S22,通过残差网络来实现塑料瓶颜色的特征提取。在残差网络中,为了提高精度并减少计算量,设置有四个残差块,记为RB1、RB2、RB3、RB4。每个残差块中有三层卷积层,即先降低输入层的网络维度,再学习特征,最后还原网络维度。其中,三层卷积层分别为第三卷积层、第四卷积层和第五卷积层。第三卷积层的卷积核为1*1,步长为2,第四卷积层的卷积核为3*3,步长为1,第五卷积层的卷积核为1*1,步长为1。S22, the feature extraction of the color of the plastic bottle is realized through the residual network. In the residual network, in order to improve the accuracy and reduce the amount of calculation, there are four residual blocks, denoted as RB1, RB2, RB3, and RB4. There are three convolutional layers in each residual block, that is, the network dimension of the input layer is reduced first, then the features are learned, and finally the network dimension is restored. Among them, the three convolutional layers are the third convolutional layer, the fourth convolutional layer and the fifth convolutional layer respectively. The convolution kernel of the third convolutional layer is 1*1, the stride is 2, the convolutional kernel of the fourth convolutional layer is 3*3, the stride is 1, and the convolutional kernel of the fifth convolutional layer is 1* 1, the step size is 1.
残差块的输出计算方法如下:The output of the residual block is calculated as follows:
假设残差块的输入为X,输出为Y,各卷积层的权重矩阵分别为W1,W2,W3,第三层卷积层输出的激励函数为relu1,第四层卷积层输出的激励函数为relu2,残差块输出的激励函数为relu3。另外为了保持输入与输出维数的一致性,假设线性变换矩阵为Wt,则输出:Assuming that the input of the residual block is X, the output is Y, the weight matrices of each convolutional layer are W 1 , W 2 , W 3 , the excitation function output by the third convolutional layer is relu 1 , and the fourth convolutional layer The activation function output by the layer is relu 2 , and the activation function output by the residual block is relu 3 . In addition, in order to maintain the consistency of the input and output dimensions, assuming that the linear transformation matrix is Wt, the output:
Y=relu3([relu2(relu1(X,W1),W2)]*W3+X*Wt)Y=relu 3 ([relu 2 (relu 1 (X,W 1 ),W 2 )]*W 3 +X*Wt)
S22中图像处理为:经过残差块RB1,输出256个特征,图像尺寸为56*56;经过残差块RB2,输出512个特征,图像尺寸为28*28;经过残差块RB3,输出1024个特征,图像尺寸为14*14;经过残差块RB4,输出2048个特征,图像尺寸为7*7。The image processing in S22 is: after the residual block RB1, output 256 features, the image size is 56*56; after the residual block RB2, output 512 features, the image size is 28*28; after the residual block RB3, output 1024 features, the image size is 14*14; after the residual block RB4, 2048 features are output, and the image size is 7*7.
S23,将提取的特征图输入到全局平均池化层,再将池化特征输入到全连接层进行塑料瓶颜色分类。对RB4残差块输出的2048个7*7的特征进行全局平均池化操作,输出2048个1*1的池化特征,然后将池化特征输入到全连接层,最后得到分类结果。S23, input the extracted feature map to the global average pooling layer, and then input the pooled features to the fully connected layer for plastic bottle color classification. The global average pooling operation is performed on the 2048 7*7 features output by the RB4 residual block, and 2048 1*1 pooling features are output, and then the pooled features are input to the fully connected layer, and finally the classification result is obtained.
S24,设置训练算法中的参数,包括学习率、训练次数和迭代次数。S24, set the parameters in the training algorithm, including the learning rate, the number of training times, and the number of iterations.
S25,将训练图像数据集输入建立的网络模型,确定深度学习网络模型及模型参数。具体步骤如下:S25, input the training image data set into the established network model, and determine the deep learning network model and model parameters. Specific steps are as follows:
S251,判断是否达到设定训练次数,若达到则得出结果,否则重复S251;S251, determine whether the set number of training times is reached, and if so, obtain the result, otherwise repeat S251;
S252,判断是否达到设定迭代次数,若达到则训练结束,否则重复S251;S252, determine whether the set number of iterations is reached, if so, the training ends, otherwise repeat S251;
S253,确定深度学习网络模型及模型参数。S253, determine the deep learning network model and model parameters.
S3,将验证图像数据集输入已确定的深度学习网络模型中,检验深度学习网络模型。S3, the verification image data set is input into the determined deep learning network model, and the deep learning network model is tested.
S4,将待测图像数据集输入到深度学习网络模型中,得到待测图像数据集中塑料瓶颜色分类结果。S4, the image data set to be tested is input into the deep learning network model, and the color classification result of the plastic bottle in the image data set to be tested is obtained.
以上所述为本发明的较佳实施例而已,但本发明不应局限于该实施例和附图所公开的内容,所以凡是不脱离本发明所公开的精神下完成的等效或修改,都落入本发明保护的范围。The above description is only the preferred embodiment of the present invention, but the present invention should not be limited to the content disclosed in the embodiment and the accompanying drawings, so any equivalents or modifications accomplished without departing from the spirit disclosed in the present invention are all fall within the protection scope of the present invention.
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