CN113159150A - Branch intervention pearl sorting method based on multi-algorithm integration - Google Patents
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Abstract
A bifurcation intervention pearl sorting method based on multi-algorithm integration comprises the following steps: step 1: respectively recording pearl data sets as Class1-7 according to categories, labeling each picture, and recording the data sets according to the ratio of 6: 2: 2, dividing the ratio into a training set, a verification set and a test set; step 2: selecting three models ResNet50, SE-ResNet50 and Vgg16 which are mainstream and advanced at present for independent training, wherein the models ResNet50, SE-ResNet50 and Vgg16 can finish pearl sorting tasks and respectively store optimal models; and step 3: the obtained three optimal models are used for an arbitration system, namely divergence arbitration is carried out on prediction results given by the constructed primary system and the constructed secondary system; and 4, step 4: in an experimental verification stage, a divergence accuracy index alpha and an additional cost index beta are used as evaluation indexes of system divergence to evaluate the overall performance of a system; and 5: selecting a system combination which achieves higher sorting precision and requires less labor cost as a final pearl sorting system according to the evaluation indexes in the step four; step 6: and outputting a final classification result. The invention finds out the error prediction possibly existing in machine classification through divergence based on a plurality of redundant algorithms, and corrects the prediction output with the intervention of least labor cost, thereby improving the classification precision of the whole pearl sorting.
Description
Technical Field
The invention relates to a pearl detection and classification method which is suitable for solving the problem that pure deep learning is limited in improvement of pearl sorting precision by utilizing a human intervention scheme.
Background
Traditional pearl sorting is mainly performed manually, and practitioners with abundant experience observe various appearance characteristics of pearls such as color, luster, shape, texture and the like through naked eyes, and then classify the pearls according to certain predetermined grade rules. It is easy to recognize that the observation and sorting of a large number of pearls whose appearance differences may not be too great is a repeated and tedious work; and with the increase of time, the fatigue of people can greatly influence the sorting accuracy. However, misclassifying a high-grade pearl as a low grade inherently reduces the value of the sale, and conversely, misclassifying a low-grade pearl as a high grade also affects the quality and the reputation of the product. Therefore, it is necessary to introduce an automated sorting method for efficient and stable sorting.
Since pearl sorting is mainly based on pearl appearance, automated sorting methods are therefore mostly based on methods and techniques of machine vision. The manual marking of pearl features in the traditional computer vision method depends on expert knowledge, and the cost is relatively high. In recent years, deep learning methods, particularly convolutional neural networks CNN, have been highly successful in image classification, which can automatically generate useful features, thereby saving a lot of manual labeling work. The current automatic classification system trained by the deep learning method achieves the sorting precision of 92.57%, but the requirement of enterprises is still insufficient. For pearl production enterprises, the improvement of the sorting accuracy by 1 percent also has great economic value. The price of the pearls is high, the price of the pearls in different grades is even different by hundreds of times, the quality of the pearls with flaws is seriously lost when the pearls with superior grades are divided into inferior grades, and the quality of the pearls with flaws is seriously lost when the pearls with flaws are divided into superior grades. Therefore, the method provides a practical demand for improving the pearl sorting accuracy.
It will be appreciated that further improvements in accuracy on this basis present significant difficulties. On one hand, the reasons are technical, and the current artificial intelligence technology based on deep learning still has the defects of difficult interpretation, poor robustness and the like, so that an algorithm with higher precision is difficult to train, and the existing error output cannot be predicted. On the other hand, the reason is that the cost is high, and a large number of accurately labeled pearl data sets are difficult to obtain. The analysis means that if the pearl sorting accuracy needs to be further improved, the existing pure deep learning technical framework is likely to need to be jumped out.
Disclosure of Invention
The invention provides a bifurcation intervention pearl sorting method based on multi-algorithm integration, which aims to overcome the defects of the prior art, namely the practical requirement on pearl sorting accuracy and the limitation of improving the accuracy under the existing framework.
The pearl sorting method based on the multiple redundant algorithms can use a method based on the multiple redundant algorithms without changing the algorithms, pearls which are judged to be branched by the multiple algorithms are identified as sorting errors by introducing the redundant automatic sorting algorithms, and the parts with the sorting errors are judged again with the least labor cost, so that an idea is provided for improving the precision of the pearl sorting method.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a bifurcation intervention pearl sorting method based on multi-algorithm integration comprises the following steps:
the method comprises the following steps: respectively recording pearl data sets as Class1-7 according to categories, labeling each picture, and recording the data sets according to the ratio of 6: 2: 2, dividing the ratio into a training set, a verification set and a test set;
step two: selecting three models ResNet50, SE-ResNet50 and Vgg16 which are mainstream and advanced at present for independent training, wherein the models ResNet50, SE-ResNet50 and Vgg16 can finish pearl sorting tasks and respectively store optimal models;
step three: the obtained three optimal models are used for an arbitration system, namely divergence arbitration is carried out on prediction results given by the constructed primary system and the constructed secondary system;
step four: in an experimental verification stage, a divergence accuracy index alpha and an additional cost index beta are used as evaluation indexes of system divergence to evaluate the overall performance of a system;
step five: selecting a system combination which achieves higher sorting precision and requires less labor cost as a final pearl sorting system according to the evaluation indexes in the step four;
step six: and outputting a final classification result.
The pearl sorting method based on the multiple redundant algorithms is provided under the condition that the algorithms are not changed, the pearl which is judged to be branched by the multiple algorithms is identified as a sorting fault by introducing the redundant automatic sorting algorithm, and the part with the sorting fault is judged again with the least labor cost, so that an idea is provided for improving the precision of the pearl sorting method. The convolutional neural network CNN can extract the features in the pictures and identify the information in the new pictures, so that the pictures with unknown labels can be classified after a large number of pictures with known labels are trained; the effective intervention of the human can correct the prediction deviation problem existing in the system.
Compared with the prior art, the technical scheme of the invention has the advantages that:
(1) the situation that only one type of algorithm is used for predicting classification errors can be detected more simply and effectively by utilizing the divergence of a plurality of redundant algorithms;
(2) a small amount of labor cost is introduced to correct the error classification when necessary, the advantages of people are effectively utilized to make up for the machine errors, and higher sorting precision is easy to obtain.
Drawings
FIG. 1: a flow chart of the method of the invention;
FIG. 2: the branch arbitration block diagram of the method of the invention;
FIG. 3: the method is based on a human bifurcation intervention block diagram of a multi-algorithm;
FIG. 4: the method is based on a divergence intervention block diagram of algorithm integration;
FIG. 5: a data set for use in the method of the invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail below with reference to the accompanying drawings and examples.
A bifurcation intervention pearl sorting method based on multi-algorithm integration comprises the following steps:
the method comprises the following steps: respectively recording pearl data sets as Class1-7 according to categories, labeling each picture, and recording the data sets according to the ratio of 6: 2: 2, dividing the ratio into a training set, a verification set and a test set;
step two: three models ResNet50, SE-ResNet50 and Vgg16 which are mainstream and advanced at present are selected for independent training, pearl sorting tasks can be completed, and the optimal models are respectively stored: setting the maximum training times to be 10 epochs when training the model, setting the single processing sample number Batch during training and testing to be 32, setting the learning rate to be 0.0001, using an Adam optimizer, setting the step-size to be 5, setting the gamma to be 0.1, gradually changing the learning rate to be 0.1 after 5 changes in the training process, and finally obtaining the accuracy of the three optimal models to be 91.84%, 92.25% and 91.29% respectively;
step three: the obtained three optimal models are used for an arbitration system, namely divergence arbitration is carried out on prediction results given by the constructed primary system and the constructed secondary system: if the divergence occurs, the system classification is considered to be possibly wrong, and an interventionalist is needed to make a final decision; if there is no divergence, the prediction result of the main algorithm is retained. Here, the three models are grouped in pairs, the remaining model serves as a main system, the collocated two models form a subsystem by an integrated learning method, and the final prediction output of the subsystem obtained by integration is defined as:
wherein h isi(x) Is the probability output result of the ith model, T is the number of models, wiIs a single model hiRight of (1)
Step four: in the experimental verification stage, the divergence accuracy index alpha and the additional cost index beta are used as evaluation indexes of system divergence to evaluate the overall performance of the system: the divergence accuracy index alpha refers to the ratio of the number of the corrected pictures after the classification error of the main algorithm to the total number of the pictures of the classification error of the model, and is used for evaluating the capability of the method for finding out the classification error; the additional cost index beta refers to the ratio of the number of pictures which are correctly classified by the main algorithm and are submitted to a person for judgment to the total number of pictures which are correctly classified by the optimal model, and is used for representing the invalid human cost consumed by the method;
step five: selecting a system combination which achieves higher sorting precision and requires less labor cost as a final pearl sorting system according to the evaluation indexes in the step four;
step six: outputting a final classification result: if the arbiter selects the classification result given by the main system as the pearl type, otherwise, the arbiter gives the prediction result again after the classification is judged by the person, and the result is considered as the final type.
The pearl data set classification step specifically comprises the following steps: first, classification according to the rough rule can be roughly divided into two categories: 1) one type is the low value flat or deep defect pearl; 2) the other is pearl with smaller flaw degree; secondly, according to a more refined classification standard, the pearl classification with poor first major classification can be divided into three minor classifications: A1) a pearl having a plurality of flat surfaces; A2) pearls of symmetrical shape; A3) other pearls; the second major category of better pearl classification can be divided into four subclasses: B1) pearls having a ratio of minor to major radii greater than 0.7; B2) a light colored pearl; B3) pearls with hidden speckles; B4) the rest of pearl.
The Convolutional Neural Network and Pearl data set referred to herein are disclosed in "Automatic Pearl Classification Machine base ON a Multistream conditional Neural Network" of IEEE TRANSACTIONS ON INDUSTRIAL ELECTRICAL 2018 by Qi Xuan, Binwei Fang, et al.
The invention finds out the error prediction possibly existing in machine classification through divergence based on a plurality of redundant algorithms, and corrects the prediction output with the intervention of least labor cost, thereby improving the classification precision of the whole pearl sorting.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (2)
1. A bifurcation intervention pearl sorting method based on multi-algorithm integration comprises the following steps:
the method comprises the following steps: respectively recording pearl data sets as Class1-7 according to categories, labeling each picture, and recording the data sets according to the ratio of 6: 2: 2, dividing the ratio into a training set, a verification set and a test set;
step two: three models ResNet50, SE-ResNet50 and Vgg16 which are mainstream and advanced at present are selected for independent training, pearl sorting tasks can be completed, and the optimal models are respectively stored: setting the maximum training times to be 10 epochs when training the model, setting the single processing sample number Batch during training and testing to be 32, setting the learning rate to be 0.0001, using an Adam optimizer, setting the step-size to be 5, setting the gamma to be 0.1, gradually changing the learning rate to be 0.1 after 5 changes in the training process, and finally obtaining the accuracies of the three optimal models to be 91.84%, 92.25% and 91.29% respectively;
step three: the obtained three optimal models are used for an arbitration system, namely divergence arbitration is carried out on prediction results given by the constructed primary system and the constructed secondary system: if the divergence occurs, the system classification is considered to be possibly wrong, and an interventionalist is needed to make a final decision; if there is no divergence, the prediction result of the main algorithm is retained. Here, the three models are grouped in pairs, the remaining model is used as a main system, the two collocated models form a subsystem by an ensemble learning method, and the final prediction output of the subsystem obtained by integration is defined as:
wherein h isi(x) Is the probability output result of the ith model, T is the number of models, wiIs a single model hiIs usually given a weight of
Step four: in the experimental verification stage, the divergence accuracy index alpha and the additional cost index beta are used as evaluation indexes of system divergence to evaluate the overall performance of the system: the divergence accuracy index alpha refers to the ratio of the number of the corrected pictures after the classification error of the main algorithm to the total number of the pictures of the classification error of the model, and is used for evaluating the capability of the method for finding out the classification error; the additional cost index beta refers to the ratio of the number of pictures which are correctly classified by the main algorithm and are submitted to a person for judgment to the total number of pictures which are correctly classified by the optimal model, and is used for representing the invalid human cost consumed by the method;
step five: selecting a system combination which achieves higher sorting precision and requires less labor cost as a final pearl sorting system according to the evaluation indexes in the step four;
step six: outputting a final classification result: if the arbiter selects the classification result given by the main system as the pearl type, otherwise, the arbiter gives the prediction result again after the classification is judged by the person, and the result is considered as the final type.
2. The method for detecting and classifying pearls according to claim 1, wherein: step one, the pearl data set classification step specifically comprises: first, classification according to the rough rule can be roughly divided into two categories: 1) one type is the low value flat or deep defect pearl; 2) the other is pearl with smaller flaw degree; secondly, according to a more refined classification standard, the pearl classification with poor first major classification can be divided into three minor classifications: A1) a pearl having a plurality of flat surfaces; A2) pearls of symmetrical shape; A3) other pearls; the second major category of better pearl classification can be divided into four subclasses: B1) pearls having a ratio of minor to major radii greater than 0.7; B2) a light colored pearl; B3) pearls with hidden speckles; B4) the rest of pearl.
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