CN111709452B - Method for evaluating surface defect model of wine bottle, electronic device and storage medium - Google Patents

Method for evaluating surface defect model of wine bottle, electronic device and storage medium Download PDF

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CN111709452B
CN111709452B CN202010434749.2A CN202010434749A CN111709452B CN 111709452 B CN111709452 B CN 111709452B CN 202010434749 A CN202010434749 A CN 202010434749A CN 111709452 B CN111709452 B CN 111709452B
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邓辅秦
李伟科
陈旭林
黄永深
冯华
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Abstract

The invention discloses an evaluation method of a wine bottle surface defect model, and discloses an electronic device and a computer readable storage medium with the evaluation method of the wine bottle surface defect model, wherein the evaluation method of the wine bottle surface defect model comprises the following steps: obtaining a training data set based on the acquired surface picture of the wine bottle; inputting a training data set into a model to be evaluated to obtain surface defect information representing surface defects corresponding to the surface pictures of the wine bottle, wherein the surface defect information of the surface pictures of the wine bottle comprises category information and detection rectangular frames, the category information represents categories of the surface defects, and the detection rectangular frames represent positions of the surface defects; according to the surface defect information evaluation model to be evaluated, the embodiment of the invention can accurately and intuitively evaluate the convolutional neural network for detecting the surface defects of the wine bottle.

Description

Method for evaluating surface defect model of wine bottle, electronic device and storage medium
Technical Field
The invention relates to the technical field of surface defect detection of wine bottles, in particular to an evaluation method, an electronic device and a storage medium of a surface defect model of a wine bottle.
Background
In the industrial production process of bottled wine bottles, the quality of raw materials, the design scheme of wine bottle drawings, the quality of processing technology (filling) and machine tool equipment, the production environment and other factors can influence, various surface defects possibly exist in the finally formed bottled wine to influence the overall product quality, the requirements of consumers on industrial products are continuously improved, the consumption desire of the consumers is not limited to the quality of the products, and the appearance and visual effect of the products are additionally required, so the quality inspection work of the surface defects of the bottled wine bottles is particularly important for the wine brewing industry, and the quality inspection capability of the surface defects of the bottled wine bottles can influence the sales prospect of the bottled wine to a certain extent.
Before the convolutional neural network model is used for detecting the surface defects of the wine bottle, training is needed to be carried out on the convolutional neural network model, an evaluation method of the convolutional neural network model aiming at the surface defects of the wine bottle is needed to be matched in the training process, various evaluation methods aiming at the convolutional neural network model exist today, but no visual evaluation method of the convolutional neural network model for detecting the surface defects of the wine bottle exists yet.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an evaluation method of a winebottle surface defect model, which can accurately and intuitively evaluate a convolutional neural network for detecting the surface defects of the winebottle.
The invention also provides an electronic device with the evaluation method of the wine bottle surface defect model.
The invention also provides a computer readable storage medium with the evaluation method of the wine bottle surface defect model.
According to an embodiment of the first aspect of the invention, the method for evaluating the surface defect model of the wine bottle comprises the following steps:
obtaining a training data set based on the acquired surface picture of the wine bottle;
inputting the training data set into a model to be evaluated to obtain surface defect information representing surface defects corresponding to the surface pictures of the wine bottle, wherein the surface defect information of the surface pictures of the wine bottle comprises category information and detection rectangular frames, the category information represents categories of the surface defects, and the detection rectangular frames represent positions of the surface defects;
and evaluating the model to be evaluated according to the surface defect information.
The method for evaluating the surface defect model of the wine bottle has at least the following beneficial effects: firstly, inputting collected surface pictures of the wine bottle into a to-be-evaluated model, detecting the surface pictures of the wine bottle by the to-be-evaluated model, detecting surface defects in the surface pictures of the wine bottle by using a rectangular detection frame, identifying the types of the surface defects, outputting class information of the surface defects and the rectangular detection frame, and finally calculating to obtain the score of the to-be-evaluated model according to the rectangular detection frame and the class information output by the to-be-evaluated model, so that a score value enables a user to more intuitively and accurately know the detection capability of the currently trained to the surface defects of the wine bottle by the to-be-evaluated model.
According to some embodiments of the invention, the evaluating the model to be evaluated according to the surface defect information comprises the steps of:
calculating IoU threshold according to a preset marked rectangular frame;
calculating IoU values according to the detection rectangular frame and the preset labeling rectangular frame;
calculating an average accuracy AP value from the IoU value and the IoU threshold value;
and calculating the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect.
According to some embodiments of the invention, the calculating IoU threshold according to the preset labeling rectangle frame adopts the following formula:
Figure SMS_1
therein, ioU t As a result of the IoU threshold value,
Figure SMS_2
and the number of pixels of the short side of the preset marked rectangular frame is the preset number.
According to some embodiments of the invention, the calculating the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect uses the following formula:
Figure SMS_3
wherein score is the score of the score,
Figure SMS_4
for the category subscript value of the surface defect, < >>
Figure SMS_5
For the total number of categories corresponding to the training dataset, < >>
Figure SMS_6
Is->
Figure SMS_7
-said weight value of said surface defect like, ->
Figure SMS_8
The average accuracy AP value for the surface defect of class i.
According to some embodiments of the invention, the wine bottle surface picture comprises at least one of the surface defects.
An electronic device according to an embodiment of the second aspect of the present invention includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for evaluating a surface defect model of a wine bottle according to any one of the first aspects of the invention when the program is executed.
The electronic device of the embodiment of the invention executes the method for evaluating the surface defect model of the wine bottle according to any one of the first aspect of the invention, so that the electronic device has all the beneficial effects of the first aspect of the invention.
According to an embodiment of the third aspect of the present invention, there is stored computer-executable instructions for performing the method for evaluating a surface defect model of a wine bottle according to any one of the first aspect of the present invention.
Since the computer-readable storage medium of the embodiment of the present invention stores thereon the computer-executable instructions for executing the method for evaluating a surface defect model of a wine bottle according to any one of the first aspect of the present invention, all the advantageous effects of the first aspect of the present invention are achieved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flowchart of an evaluation method of a surface defect model of a wine bottle according to an embodiment of the present invention;
FIG. 2 is a flowchart showing a step of evaluating a model to be evaluated according to surface defect information in the method for evaluating a surface defect model of a wine bottle according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Reference numerals:
an electronic device 100, a processor 101, a memory 102.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, greater than, less than, etc., are understood to not include the present number.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
The invention provides an evaluation method, an electronic device and a storage medium of a wine bottle surface defect model, a to-be-trained data set comprises a plurality of wine bottle surface pictures to be trained, each picture comprises at least one surface defect, after the pictures are input into a convolutional neural network, the convolutional neural network detects the positions of the surface defects in the pictures and frames the surface defects out by using detection rectangular frames, the pictures also comprise surface defects which are marked with the rectangular frames in advance, the marking rectangular frames represent the actual positions of the surface defects, then a IoU threshold value is calculated according to the size of short side pixels of the marking rectangular frames, different IoU threshold values are used according to the surface defects with different sizes, ioU values of the detection rectangular frames and the marking rectangular frames are calculated, if IoU values are larger than IoU threshold values, the pictures are considered to be detected as positive examples, if IoU values are smaller than IoU threshold values, the pictures are negative examples, the average accuracy AP value of each type of the surface defects is calculated and obtained, finally the average accuracy AP value of the surface defects of each type of the to be evaluated model is calculated, the to obtain the to-be-evaluated model, the visual evaluation model which has better evaluation performance of the surface defects to be evaluated is better than the user, and the current model to be evaluated, and the user has better evaluation performance of the surface defect to be evaluated, and has better evaluation performance to be better can be evaluated by the user.
Referring to fig. 3, an electronic device 100 according to an embodiment of the first aspect of the present invention includes a memory 102 and a processor 101, where one processor 101 and one memory 102 are illustrated in fig. 3.
The processor and the memory may be connected by a bus or otherwise, for example in fig. 3.
The memory 102, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory 102 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some implementations, the memory 102 may optionally include memory 102 that is remotely located relative to the processor, which may be connected to the electronic device 100 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Those skilled in the art will appreciate that the device structure shown in fig. 3 is not limiting of the electronic device 100 and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
Referring to fig. 1 and 3, in the electronic device according to the embodiment of the first aspect of the present invention, the processor 101 in the electronic device 100 may be configured to invoke the evaluation method of the bottle surface defect model stored in the memory 102, and perform the following steps:
s110, obtaining a training data set based on the acquired surface picture of the wine bottle;
s120, inputting the training data set into a model to be evaluated to obtain surface defect information representing the surface defects corresponding to the surface pictures of the wine bottle,
the surface defect information of the surface picture of the wine bottle comprises category information and a detection rectangular frame, wherein the category information represents the category of the surface defect, and the detection rectangular frame represents the position of the surface defect;
s130, evaluating the model to be evaluated according to the surface defect information.
Based on the hardware structure of the electronic device, various embodiments of an evaluation method of a surface defect model of a wine bottle are provided.
Referring to fig. 1, a method for evaluating a surface defect model of a wine bottle according to a second aspect of the present invention includes:
s110, obtaining a training data set based on the acquired surface picture of the wine bottle;
s120, inputting the training data set into a model to be evaluated to obtain surface defect information representing the surface defects corresponding to the surface pictures of the wine bottle,
the surface defect information of the surface picture of the wine bottle comprises category information and a detection rectangular frame, wherein the category information represents the category of the surface defect, and the detection rectangular frame represents the position of the surface defect;
s130, evaluating the model to be evaluated according to the surface defect information.
In this embodiment, the to-be-trained data set includes ten surface defects, including bottle cap breakage, bottle cap deformation, bottle cap edge breakage, bottle cap screwing, bottle cap breakpoint, label skew, label wrinkling, label bubble, code spraying normal, code spraying abnormal, the collected surface picture of the wine bottle is input into the to-be-evaluated model, then the to-be-evaluated model detects the surface picture of the wine bottle, the surface defect in the surface picture of the wine bottle is detected by the detection rectangular frame, the type of the surface defect is identified, the type information representing the type of the surface defect is output, and finally the score of the to-be-evaluated model is calculated according to the detection rectangular frame and the type information output by the to-be-evaluated model, so that the user can intuitively and accurately know the detection capability of the current to-be-evaluated model according to the final score value.
Referring to fig. 2, in the present embodiment, step S130 of evaluating the model to be evaluated based on the surface defect information includes the steps of:
s131, calculating a IoU threshold according to a preset marked rectangular frame;
s132, calculating IoU values according to the detection rectangular frame and a preset marked rectangular frame;
s133, calculating an average accuracy AP value according to the IoU value and the IoU threshold value;
s134, calculating the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect.
Before inputting the surface picture of the wine bottle into the model to be evaluated, the labeling rectangular frame is manually used for outputting the surface defect in the surface picture of the wine bottle, so that the labeling rectangular frame represents the actual position of the surface defect, the model to be evaluated calculates different IoU thresholds according to each labeling rectangular frame, so that different IoU thresholds are set for each surface defect, the influence of two common methods of fixing IoU thresholds and fixing IoU thresholds for defect types on the model detection precision can be reduced, and the accuracy of evaluating the model to be evaluated is improved;
then, calculating IoU values according to the detected rectangular frames and the marked rectangular frames, calculating an average accuracy AP value according to IoU values and IoU threshold values, if IoU values are larger than IoU threshold values, judging that the detection is successful, judging that the detection is positive, if IoU values are smaller than IoU threshold values, judging that the detection is failed, judging that the detection is negative, and calculating to obtain the average accuracy AP value of the model to be evaluated on the surface defects by counting the number of positive cases of each type of surface defects;
and finally, obtaining the score of the convolutional neural network according to the average accuracy AP value of each type of surface defect and the weight value of the type of surface defect, wherein the weight value of each type of surface defect is determined by the tolerance of an enterprise to each type of surface defect instead of being calculated by purely using a summation average mode, so that the final evaluation score of the embodiment is more accurate and visual, and meets the requirements of a real enterprise, and the weight value of each type of surface defect is shown in a table 1.
Table 1 weight values for surface defects of each type of bottled wine bottle
Figure SMS_9
Referring to fig. 2, in the present embodiment, step S131 calculates IoU threshold according to a preset labeling rectangular frame by using the following formula:
Figure SMS_10
therein, ioU t Is a value of IoU threshold value,
Figure SMS_11
the number of pixels of the short side of the preset marked rectangular frame is calculated according to the size of the surface defect, so that the IoU threshold value is calculated according to the size of the surface defect, and compared with the fixed IoU threshold value or the fixed IoU threshold value adopted by each type of surface defect, the embodiment adopts different IoU threshold values according to the size of each surface defect, so that the average accuracy AP value calculated later is more accurate.
Referring to fig. 2, in the present embodiment, step S134 calculates the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect by the following formula:
Figure SMS_12
wherein score is a score of,
Figure SMS_13
is the category subscript value of the surface defect, +.>
Figure SMS_14
For the total number of categories corresponding to the training dataset, < +.>
Figure SMS_15
Is->
Figure SMS_16
Weight value of surface defect class, +.>
Figure SMS_17
For the average accuracy AP value of the i-th type surface defects, the weight value of each type surface defect is +.>
Figure SMS_18
As shown in Table 1, the final score value combines the average accuracy AP value of each type of defect and the weight value of the type of surface defect, so that the score value is more accurate and more in line with the actual requirements of enterprises, and simultaneously, the score value enables a user to intuitively know the detection capability of the currently trained model to be evaluated, and the user can easily and conveniently evaluate the model by>
Figure SMS_19
The detection capability of the model to be evaluated on a certain type of surface defects can be known.
In this embodiment, the surface picture of the wine bottle includes at least one surface defect, if there are a large number of normal pictures in the training data set, the normal pictures do not have any information of the surface defect of the wine bottle to be detected, and the training data set includes at least one feature of the normal picture, so if there are a large number of normal pictures in the training data set, the detection accuracy of the model to be evaluated is unnecessarily reduced, and the score is also reduced, but the score is caused by insufficient training data set, so the score does not help in improving the model to be evaluated, and therefore, in order to improve the evaluation accuracy of the model by the final score, the pictures in the training data set include at least one surface defect feature.
The computer readable storage medium according to the embodiment of the third aspect of the present invention stores computer executable instructions for performing the method for evaluating a surface defect model of a wine bottle according to the embodiment of the second aspect.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present invention.

Claims (4)

1. The method for evaluating the surface defect model of the wine bottle is characterized by comprising the following steps of:
obtaining a training data set based on the acquired surface picture of the wine bottle;
inputting the training data set into a model to be evaluated to obtain surface defect information representing surface defects corresponding to the surface pictures of the wine bottle, wherein the surface defect information of the surface pictures of the wine bottle comprises category information and detection rectangular frames, the category information represents categories of the surface defects, and the detection rectangular frames represent positions of the surface defects;
evaluating the model to be evaluated according to the surface defect information;
wherein, the evaluating the model to be evaluated according to the surface defect information comprises the following steps:
calculating IoU threshold according to a preset marked rectangular frame;
calculating IoU values according to the detection rectangular frame and the preset labeling rectangular frame;
calculating an average accuracy AP value from the IoU value and the IoU threshold value;
calculating the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect;
the IoU threshold is calculated according to a preset marked rectangular frame by adopting the following formula:
Figure QLYQS_1
therein, ioU t As a result of the IoU threshold value,
Figure QLYQS_2
the number of pixels of the short side of the preset marked rectangular frame is the number of pixels of the short side of the preset marked rectangular frame;
and calculating the score of the model to be evaluated according to the average accuracy AP value and the weight value of the surface defect by adopting the following formula:
Figure QLYQS_3
wherein score is the score of the score,
Figure QLYQS_4
for the category subscript value of the surface defect, < >>
Figure QLYQS_5
For the total number of categories corresponding to the training dataset, < >>
Figure QLYQS_6
Is->
Figure QLYQS_7
-said weight value of said surface defect like, ->
Figure QLYQS_8
The average accuracy AP value for the surface defect of class i.
2. The method of claim 1, wherein the surface picture of the wine bottle comprises at least one of the surface defects.
3. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the program, implements the method for evaluating a surface defect model of a wine bottle according to any one of claims 1 to 2.
4. A computer-readable storage medium storing computer-executable instructions, characterized in that: the computer-executable instructions are for performing the method of evaluating a wine bottle surface defect model according to any one of claims 1 to 2.
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