CN115273072B - Apple leaf disease detection method based on improved Yolov5s model - Google Patents
Apple leaf disease detection method based on improved Yolov5s model Download PDFInfo
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Abstract
The invention discloses an apple leaf disease detection method based on an improved Yolov5s model, which comprises the steps of firstly, obtaining images of different diseases of apple leaves, labeling, and constructing an initial data set; expanding a data set by adopting an image processing method, and dividing a training set and a testing set to obtain a final data set; introducing a Yolov5s model, improving, and training a data set by using the improved model to obtain a final apple leaf disease detection model; and detecting the image to be detected by using a final apple leaf disease detection model to obtain a detection result. According to the invention, through improving the Yolov5s model and introducing the Attentive GAN algorithm, the expression capability of the network learning characteristic is enhanced when the model is trained on the network, the accuracy and precision of the model are improved, and the accurate identification of apple leaf diseases can be realized under the rainy day condition.
Description
Technical Field
The invention belongs to the technical field of plant disease detection, and particularly relates to an apple leaf disease detection method based on an improved Yolov5s model.
Background
Apples are rich in minerals and vitamins, and are one of the most commonly eaten fruits. Apple leaf is the main organ for photosynthesis, plays an important role in making nutrition and delivering nutrition to fruits, so that apple leaf diseases are important factors for influencing the growth of apple trees. The apple leaf diseases are various, wherein the common diseases include alternaria leaf spot, brown spot, gray spot, mosaic disease and rust, the fruit retention is based on leaf retention, the apple leaf diseases are rapidly found and accurately identified and detected, different treatment measures are adopted for different diseases, the production loss can be reduced, and the yield and quality of apples are improved. Therefore, the method can accurately detect and identify the apple leaf diseases, and plays an important role in ensuring the growth of apple trees.
The target detection method based on deep learning is the most popular plant disease identification method nowadays. The task of object detection is to find the object of interest in the image and to annotate the name and location size of the object. The current mainstream target detection method has poor effect on detecting small targets such as apple leaf diseases, and has low identification precision, and under the condition of shooting on non-sunny days, the detection effect of apple leaf diseases is poor due to the interference of raindrops, and various leaf diseases can not be accurately identified, so that a detection method is needed to improve the detection precision of apple leaf diseases at present.
Disclosure of Invention
The invention aims to: aiming at the defects in the prior art, the invention aims to provide the apple leaf disease detection method based on the improved Yolov5s model, which not only improves the precision and accuracy of a network training model, but also can better detect the apple leaf disease under the rainy day condition.
The technical scheme is as follows: in order to achieve the above object, the present invention adopts the following technical scheme:
an apple leaf disease detection method based on an improved Yolov5s model comprises the following steps:
s1, obtaining images of different diseases of apple leaves, and labeling to construct an initial data set;
s2, expanding a data set by adopting an image processing method, and dividing a training set and a testing set to obtain a final data set;
s3, introducing and improving a Yolov5S model, and training a data set by using the improved model to obtain a final apple leaf disease detection model;
and S4, detecting an image to be detected by using a final apple leaf disease detection model to obtain a detection result.
Further, in step S1, common diseases of apple leaves include alternaria leaf spot, brown spot, gray spot, mosaic and rust, and different disease images are labeled.
Further, in step S2, affine transformation, filtering, translation and rotation are performed on the image, and the data set is expanded to increase the richness of the data set and prevent the model from being over fitted.
Further, in step S3, FEM is added on the basis of the feature pyramid of the jack structure of the Yolov5S model, and meanwhile, a CA attention mechanism module is added after the CSP structure in the jack structure of the Yolov5S model, so that the expression capability of the network learning feature is enhanced, and the average precision mean value of the network training model is improved.
Further, the FEM comprises two parts, namely a multi-branch convolution layer and an average pooling layer, wherein the multi-branch convolution layer comprises an expansion convolution layer, a BN layer and an activation layer, and different receptive fields are provided for an input feature map through an expansion convolution method; the average pooling layer fuses traffic information of each branch receptive field, and multi-scale precision detection is improved.
Further, the implementation process of the A attention mechanism module is as follows:
in order to acquire the attention on the width and the height of an image and encode accurate position information, firstly, carrying out global average pooling on the width and the height of an input feature map to obtain feature maps of the two directions; then splicing the feature graphs in the width direction and the height direction of the obtained global receptive field together, then sending the feature graphs into a convolution module with a shared convolution kernel of 1 multiplied by 1, reducing the dimension to the original C/r, and then sending the feature graph F subjected to batch normalization processing into a Sigmoid activation function to obtain a feature graph F; then the characteristic diagram F is convolved into 1X 1 according to the original height and width to obtain the same number of channels and the original F h And f w The attention weights of the feature graphs in the height and the width are respectively obtained through a Sigmoid activation function; and finally, multiplying and adding calculation on the original feature map to finally obtain the feature map with attention weights in the width and height directions.
Further, in step S5, preprocessing an image to be detected, judging whether the image is photographed on a sunny day or a rainy day, and if so, performing raindrop noise reduction processing on the image; if the shooting is performed on a sunny day, the network model is directly used for detection, and a final detection result is obtained.
Further, the process of judging whether the image is photographed on a sunny day is as follows:
s51: judging whether the image is photographed on a sunny day or not by a method of extracting texture features and performing feature matching according to the difference of the surface textures of the blades on the sunny day and the rainy day;
s52: inputting an image to be detected, carrying out gray level and binarization processing on the detected image, extracting feature points on the surface of the blade, vectorizing the extracted key points, and taking the extracted local features as an observation chart;
s53: and carrying out similarity measurement on the model layout and the observation diagram.
Further, a threshold is set when the similarity measure satisfies d (M ij ,R ij ) When the threshold is less than the threshold, the image to be detected can be judged to be shot on a sunny day, and the detection model is directly used for detecting diseases; if the judging condition is not met, shooting the image in rainy days, and carrying out noise reduction treatment on the image by adopting an active GAN algorithm.
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
based on the original yolov5s model structure, the invention adds two innovative structures of the FEM module and the CA attention module, and the two structures can enlarge the receptive field of the feature map, improve the recognition precision of the multi-scale apple leaves, enhance the expression capability of the network learning features and improve the average precision mean value of the network training model. In addition, an innovative step of judging whether the image is photographed on a sunny day is added when the image is detected, and the detection precision of apple leaf diseases under the rainy day condition is improved by using the Attentive GAN algorithm to reduce noise when the image to be detected is photographed on a non-sunny day. According to the invention, through improving the Yolov5s model and introducing the Attentive GAN algorithm, the expression capability of the network learning characteristic is enhanced when the model is trained on the network, the accuracy and precision of the model are improved, and the accurate identification of apple leaf diseases can be realized under the rainy day condition.
Drawings
Fig. 1 is a flowchart of an apple leaf disease detection method based on an improved Yolov5s model in the present invention.
FIG. 2 is a graph of a modified Yolov5s model of the present invention;
FIG. 3 is a graph showing the results of detecting Alternaria pomacea disease by the method of the present invention;
FIG. 4 is a graph showing the detection result of brown spot disease of apple leaf by the method of the invention;
FIG. 5 is a graph showing the detection result of the apple leaf gray spot disease by the method of the invention;
FIG. 6 is a graph of the detection result of apple leaf mosaic disease by the method of the invention;
FIG. 7 is a graph showing the results of detecting rust disease of apple leaves by the method of the invention.
Detailed Description
The invention will be further illustrated by the following drawings and specific examples, which are carried out on the basis of the technical solutions of the invention, it being understood that these examples are only intended to illustrate the invention and are not intended to limit the scope of the invention.
As shown in FIG. 1, the invention discloses an apple leaf disease detection method based on an improved Yolov5S model, which comprises five steps of S1-S5:
s1, acquiring an apple leaf disease image, labeling, and constructing an initial data set;
the apple leaf disease image mainly comprises five common diseases of apple leaves: spot leaf fall, brown spot, gray spot, mosaic, rust, and labeling the image with a Make sense.
S2, performing data set expansion by using an image processing method to obtain a final data set;
the initial data set adopts an image processing method, including the existing affine transformation, filtering, translation and rotation methods, and the data set is expanded to increase the richness of the data set, prevent the model from being over-fitted and improve the generalization capability of the model. Dividing the data set into a training set and a testing set according to the proportion of 8:2 to obtain a final data set.
S3, introducing a Yolov5S model, improving a Neck structure and a Backbone structure of the model, and training by using the improved Yolov5S model to obtain a final apple leaf disease detection model.
S31, adding FEM (feature enhancement module) on the basis of a feature pyramid of a Neck structure of an original Yolov5S model, wherein the FEM comprises two parts:
the first part is a multi-branch convolutional layer, comprising three structures: an expanded convolution layer, a BN layer and a linear rectification (ReLU) activation layer, and different receptive fields are provided for an input feature map through an expanded convolution method; the second part is an average pooling layer, which fuses traffic information of each branch receptive field and improves multi-scale precision detection.
The multi-branch convolution layer provides the input characteristic diagram with the receptive fields with different sizes by a method of expansion convolution, and expansion convolutions in three divided structural layers all have kernels with the same size but different expansion speeds. Specifically, they all use 3×3 dilation convolution kernels, with three levels of dilation speed d of 1, 3, 5, respectively. The spreading convolution can spread the receptive field in an exponential ratio without losing resolution. Whereas in the convolution operation of the dilation convolution, the elements of the convolution kernel are spaced apart, the size of the space depends on the expansion rate, unlike in standard convolution operations where the elements of the convolution kernel are all adjacent. The convolution kernel is changed from 3×3 to 7×7, and the receptive field of the layer is 7×7. The receptive field formula of the dilation convolution is:
r i ＝1+(k-1)×d
r 2 ＝r 1 +(k-1)×d
r n Δr n-1 +(k-1)×d
where k and d represent the steps of the kernel-size and convolution, respectively.
And the average pooling layer fuses the receptive field information of each branch, and finally, the multi-scale precision prediction can be improved. The branch pooling layer is used for fusing information from different parallel branches, and avoids introducing additional parameters. During training, the representations of the different parallel branches are balanced using an averaging operation, enabling a single branch to perform reasoning during testing. The expression is as follows:
wherein y is i Representing the output of the branch pooling layer, a represents the number of parallel branches.
And S32, adding a CA attention mechanism module after the CSP structure in the back bone structure of the original Yolov5S model so as to enhance the expression capability of the network learning characteristics and improve the average precision mean value of the network training model.
The CA attention module implementation proceeds as follows.
In order to acquire the attention on the width and the height of an image and encode accurate position information, a CA module carries out global average pooling on the width and the height of an input feature map to obtain the feature map of the two directions, and the formula is as follows:
where W is the width of the input feature map, H is the height of the input feature map, x c Representing the input at the c-th channel, h represents the h height at the feature map, x c (h, i) represents an input at h height dividing width W into W bisecting ith bisection, x c (j, w) denotes dividing the height H into H bisectors at the j-th bisector at the width wAnd (5) inputting.
Then splicing the feature graphs in the width direction and the height direction of the obtained global receptive field together, then sending the feature graphs into a convolution module with a shared convolution kernel of 1 multiplied by 1, reducing the dimension to the original C/r, and then sending the feature graph F subjected to batch normalization processing into a Sigmoid activation function to obtain a feature graph F, wherein the formula is as follows:
wherein δ represents a Sigmoid activation function, and the formula of the activation function is as follows:
then the characteristic diagram F is convolved into 1X 1 according to the original height and width to obtain the same number of channels and the original F h And f w The attention weights of the feature map in height and width are obtained through a Sigmoid activation function respectively, and the formulas are as follows:
ω h ＝δ(f h (F h ))
ω w ＝δ(f w (F w ))
where δ is a Sigmoid activation function, F h And F w The components of the feature map F in the height and width directions, F h And f w The two components of the feature map F are respectively subjected to a 1×1 convolution.
Finally, the original feature map is multiplied to obtain the feature map with attention weight in the width and height directions, and the formula is as follows:
wherein x is c (m, n) is the original feature map,
and->
The height and width directions are feature maps with attention weights, respectively.
S4, training the data set by using the improved Yolov5S model to obtain a final apple leaf disease detection model;
the training data set and the test data set are transmitted into an improved Yolov5s model, and the following parameters are set: the input image size was set to 640 x 640, batch_size was set to 4, the training number epoch was set to 100, and then training of the model was performed. The invention verifies the performance of the improved Yolov5s model and the original Yolov5s model, and the experimental environment is as follows: CPU: intel (R) Core (TM) i5-7300HQ, 16g of memory, GTX1050 as a display card, 11.6 as a Cuda version, and Pytorch as a framework. The experimental results are as follows.
The mAP value of the improved yolov5s model is 95.8%, the mAP value of the original yolov5s is 89.7%, and the improvement is 6.1 percent; the improved yolov5s model has an accuracy value of 93.1%, the original yolov5s model has an accuracy value of 87.6%, and the accuracy value is improved by 5.5 percent; the recall rate of the improved yolov5s model is 94.1%, the recall rate of the original yolov5s model is 90.3%, 3.8% are improved, and comprehensive results show that the improved yolov5s model is superior to the original yolov5s model in a plurality of performance evaluation indexes.
S5, preprocessing an image to be detected, judging whether the image is shot in a sunny day or a rainy day, and if so, performing raindrop noise reduction processing on the image by using an active GAN algorithm; if the shooting is performed on a sunny day, the network model is directly used for detection, and a final detection result is obtained.
The process of judging whether the image is shot on a sunny day is as follows:
s51: according to the difference of the surface textures of the blades in sunny days and rainy days, whether the image is shot in sunny days or not can be judged by a method of extracting texture features and performing feature matching. Firstly, an apple leaf image shot in sunny days is read as matching data, gray processing and binarization processing are carried out on the matching data image, then, characteristic points of the leaf are extracted by adopting a SIFT algorithm, extracted key points are vectorized, and extracted local characteristics are used as a matching template. The template map key point vectorization matrix is as follows:
s52: then inputting an image to be detected, carrying out gray level and binarization processing on the detected image, extracting feature points on the surface of the blade by adopting a SIFT algorithm, vectorizing the extracted key points, and taking the extracted local features as an observation chart. The observation map keypoint vectorization matrix is as follows:
s53: and carrying out similarity measurement on the model layout and the observation diagram, wherein the measurement formula is as follows:
wherein m is ij Feature value r representing each key point in template vectorization matrix ij Representing the eigenvalues of each key point in the observation map vectorization matrix.
A threshold is set, when the similarity metric satisfies d (M ij ,R ij ) When the threshold is less than the threshold, the image to be detected can be judged to be shot on a sunny day, and the detection model is directly used for detecting diseases; if the judging condition is not met, shooting the image in rainy days, and carrying out noise reduction treatment on the image by adopting an active GAN algorithm.
The invention puts the preprocessed image into the apple leaf disease detection model, outputs the detection result, and the detection result diagrams are shown in figures 3-7, and are respectively the detection results of Alternaria alternata (Alternaria Boltch), brown Spot (brown Spot), gray Spot (grey Spot), mosaic disease (Mosaic disease) and Rust disease (Rust), so that the apple leaf disease detection model can accurately identify apple leaf diseases.
The detailed description is merely a preferred embodiment of the invention and is not intended to limit the scope of the invention, which is defined by the appended claims.
Claims (7)
1. An apple leaf disease detection method based on an improved Yolov5s model is characterized by comprising the following steps of: the method comprises the following steps:
s1, obtaining images of different diseases of apple leaves, and labeling to construct an initial data set;
s2, expanding a data set by adopting an image processing method, and dividing a training set and a testing set to obtain a final data set;
s3, introducing and improving a Yolov5S model, and training a data set by using the improved model to obtain a final apple leaf disease detection model;
based on a feature pyramid of a rock structure of the Yolov5s model, adding FEM, and simultaneously adding a CA attention module behind a CSP structure in a back bone structure of the Yolov5s model, so as to enhance the expression capability of the network learning features and improve the average precision mean value of the network training model; the CA attention module is realized as follows:
in order to acquire the attention on the width and the height of an image and encode accurate position information, the CA attention module carries out global average pooling on the width and the height of an input feature map to obtain the feature map of the two directions, and the formula is as follows:
wherein,,w is the width of the input feature map, H is the height of the input feature map, x c Representing the input at the c-th channel, h represents the h height at the feature map, x c (h, i) represents an input at h height dividing width W into W bisecting ith bisection, x c (j, w) represents an input at the j-th aliquot dividing the height H into H aliquots at the w-width;
then splicing the feature graphs in the width direction and the height direction of the obtained global receptive field together, then sending the feature graphs into a convolution module with a shared convolution kernel of 1 multiplied by 1, reducing the dimension to the original C/r, and then sending the feature graph F subjected to batch normalization processing into a Sigmoid activation function to obtain a feature graph F, wherein the formula is as follows:
wherein δ represents a Sigmoid activation function, and the formula of the activation function is as follows:
then the characteristic diagram F is convolved into 1X 1 according to the original height and width to obtain the same number of channels and the original F h And f w The attention weights omega of the feature graphs in height and width are obtained through a Sigmoid activation function h And omega w The formula is as follows:
ω h ＝δ(f h (F h ))
ω w ＝δ(f w (F w ))
wherein F is h And F w The components of the feature map F in the height and width directions, F h And f w Respectively a characteristic diagram of the two components of the characteristic diagram F after 1 multiplied by 1 convolution;
finally, the original feature map is multiplied to obtain the feature map with attention weight in the width and height directions, and the formula is as follows:
wherein x is c (m, n) is the original feature map,
and->
Feature maps with attention weights in the height and width directions, respectively;
and S4, detecting an image to be detected by using a final apple leaf disease detection model to obtain a detection result.
2. The apple leaf disease detection method based on an improved Yolov5s model of claim 1, wherein the method comprises the following steps: in the step S1, common diseases of apple leaves comprise Alternaria alternata, brown spot, gray spot, mosaic disease and rust, and different disease images are labeled.
3. The apple leaf disease detection method based on an improved Yolov5s model of claim 1, wherein the method comprises the following steps: in step S2, affine transformation, filtering, translation and rotation are performed on the image, and the data set is expanded to increase the richness of the data set and prevent the model from being fitted.
4. The apple leaf disease detection method based on an improved Yolov5s model of claim 1, wherein the method comprises the following steps: the FEM comprises two parts, namely a multi-branch convolution layer and an average pooling layer, wherein the multi-branch convolution layer comprises an expansion convolution layer, a BN layer and an activation layer, and different receptive fields are provided for an input feature map through an expansion convolution method; the average pooling layer fuses traffic information of each branch receptive field, and multi-scale precision detection is improved.
5. The apple leaf disease detection method based on an improved Yolov5s model of claim 1, wherein the method comprises the following steps: in step S5, preprocessing an image to be detected, judging whether the image is photographed in a sunny day or a rainy day, and if so, performing raindrop noise reduction on the image; if the shooting is performed on a sunny day, the network model is directly used for detection, and a final detection result is obtained.
6. The apple leaf disease detection method based on an improved Yolov5s model of claim 1, wherein the method comprises the following steps: the process of judging whether the image is shot on a sunny day is as follows:
s51: judging whether the image is photographed on a sunny day or not by a method of extracting texture features and performing feature matching according to the difference of the surface textures of the blades on the sunny day and the rainy day;
s52: inputting an image to be detected, carrying out gray level and binarization processing on the detected image, extracting feature points on the surface of the blade, vectorizing the extracted key points, and taking the extracted local features as an observation chart;
s53: and carrying out similarity measurement on the model layout and the observation diagram.
7. The apple leaf disease detection method based on an improved Yolov5s model of claim 6, wherein the method comprises the following steps: setting threshold, d (M ij ,R ij ) Representing a similarity measure, when the similarity measure satisfies d (M ij ,R ij ) When the threshold is less than the threshold, the image to be detected can be judged to be shot on a sunny day, and the detection model is directly used for detecting diseases; if the judging condition is not met, shooting the image in rainy days, and carrying out noise reduction treatment on the image by adopting an active GAN algorithm.
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