A plastic product detection system based on image recognition

Abstract

The present invention discloses a plastic product detection system based on image recognition, which relates to the field of plastic product detection technology, including an image acquisition module, a data storage and feature extraction module, a feature analysis and model input module, an intelligent evaluation and classification module, a homogeneous coating acquisition module, and a heterogeneous coating adjustment module: The image acquisition module first uses a scanner to illuminate the surface of the plastic product with a preset beam width and focal length to capture high-quality surface images. By introducing deep learning and image analysis technology, the present invention significantly improves the detection accuracy of uneven coatings on plastic products. The system dynamically analyzes surface texture and reflection heterogeneity, intelligently evaluates coating consistency, and adjusts the beam width and focal length according to the coating status to ensure high-quality images. This intelligent detection and adjustment strategy effectively improves detection efficiency and accuracy, reduces quality risks and production costs, and breaks through the limitations of traditional methods.

Description

Technical Field

[0001] The present invention relates to the technical field of plastic product detection, and in particular to a plastic product detection system based on image recognition. Background Art

[0002] Image recognition-based plastic product inspection is a method that uses computer vision technology to automatically inspect the quality of plastic products. Images of plastic products are captured using a high-resolution camera or scanner and fed into an image recognition system. The system uses algorithms such as deep learning and convolutional neural networks (CNNs) to process, analyze, and extract features from the images. This system can automatically detect defects in plastic products, such as cracks, bubbles, deformation, color difference, or surface contamination. Image recognition-based plastic product inspection not only improves inspection efficiency and reduces manual intervention, but also provides greater accuracy and consistency, helping to promptly identify quality issues during the production process and thus ensuring high product quality standards.

[0003] The existing technology has the following deficiencies:

[0004] In existing technologies, scanners typically illuminate the surface of plastic products with a fixed beam width and focal length. After the light is reflected, it is captured by a sensor and image data is generated. However, when the plastic surface coating is uneven, resulting in excessive reflection in certain areas, the sensor may not be able to clearly capture the details of these areas, leading to a series of serious consequences. This excessive reflection may cause the sensor to be overexposed or saturated with light, unable to properly capture the details of the reflected light, resulting in distorted or missing image data and the inability to accurately identify potential surface defects such as cracks, bubbles, stains or scratches. As a result, quality inspections may fail, and even if the plastic product has serious defects, these problems cannot be discovered in time. If these defects are not identified in a timely manner, they may enter the mass production stage and eventually enter the market, resulting in substandard products and even triggering product recalls or quality lawsuits, causing huge economic losses and brand reputation risks to the company.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a plastic product detection system based on image recognition. By introducing deep learning models and image analysis technology, this solution significantly improves the detection accuracy of uneven coatings on plastic products. Dynamically analyze the surface texture differentiation reference value and the reflection heterogeneity reference value, intelligently evaluate the coating state, and accurately divide the coating uniformity through the coating consistency index. For products with uneven coatings, the system can adjust the scanner's beam width and focal length in real time to ensure image quality and avoid the limitations of traditional methods. This intelligent detection and adjustment strategy improves detection efficiency and accuracy, reduces quality risks and costs, and solves the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a plastic product inspection system based on image recognition, comprising an image acquisition module, a data storage and feature extraction module, a feature analysis and model input module, an intelligent evaluation and classification module, a homogeneous coating acquisition module, and a heterogeneous coating adjustment module:

[0008] The image acquisition module,first, uses a scanner to illuminate the surface of the plastic product with a preset beam width and focal length,,capturing high-quality surface images;

[0009] Data storage and feature extraction module, the collected surface images are stored and organized into a structured data set, and key features that help identify coating unevenness are extracted from the data set;

[0010] The feature analysis and model input module analyzes the extracted key features within the detection window and inputs the analyzed key features into a pre-learned deep learning model. The deep learning model then performs an intelligent assessment of the coating status on the surface of the plastic product.

[0011] Intelligent evaluation and classification module, which classifies the surface conditions of plastic products into homogeneous coating and heterogeneous coating based on the evaluation results of deep learning models;

[0012] Homogeneous coating acquisition module: For plastic products that are judged to have homogeneous coatings, the scanner continues to collect surface state data with the original set beam width and focal length;

[0013] The heterogeneous coating adjustment module dynamically adjusts the scanner's beam width and focal length for plastic products judged to have heterogeneous coatings based on the deep learning model evaluation results.

[0014] Preferably, key features that help identify coating unevenness are extracted from the data set, and the extracted features include texture changes and surface reflected light gradients. Under the detection window, the extracted texture changes and surface reflected light gradients are analyzed to generate surface texture differentiation reference values and surface reflection heterogeneity reference values, respectively. The surface texture differentiation reference values quantify the degree of variation of the surface texture of the plastic product in spatial distribution and local areas, and the surface reflection heterogeneity reference values quantify the differences in reflected light intensity in different areas of the surface of the plastic product, which is specifically manifested as an uneven distribution of surface reflectivity.

[0015] Preferably, the specific steps of analyzing texture changes in the detection window and generating surface texture differentiation reference values are as follows:

[0016] In the detection window, texture features are first extracted from the image data of the plastic product surface. Texture pattern data of the local area is extracted through texture analysis. Based on the extracted texture features, the texture difference of each area in the image is calculated. The calculation expression is as follows:

[0017]

[0018] , where is the value of the texture feature dimension of region i in the image at the kth dimension, is the value of the texture feature dimension of region j in the image, α is the adjustment factor of the difference metric, N is the total number of texture features, represents the difference between region i and region j in the kth texture feature dimension, D(t i ,t j ) is the texture difference;

[0019] Based on the results of texture difference measurement, the texture differences of different regions in the image are calculated, and the differences are weighted by the weight factor. The calculation expression is as follows:

[0020]

[0021] , where Δ i is the regional texture difference, β j is the weight factor of the neighborhood region, is the weight factor of region j, is a set of neighborhood regions, is a set, representing the set of regions adjacent to region i;

[0022] Combine all regional texture differences Δ i , generate the reference value of surface texture differentiation, the calculation expression is as follows:

[0023]

[0024] , where Itexture is the reference value of surface texture differentiation, M is the total number of regions, λ i is the adjustment factor for region i.

[0025] Preferably, the specific steps of analyzing the surface reflected light gradient in the detection window and generating the surface reflectance heterogeneity reference value are as follows:

[0026] In the detection window, the reflected light data of the plastic product surface is collected by the scanner to obtain the reflected light intensity value of each sampling point. The light gradient is obtained by calculating the light intensity change of adjacent pixel points. The calculation expression is as follows:

[0027]

[0028] , where R(x,y) is the intensity of the surface reflected light, is the reflected light intensity gradient, is the rate of change of the reflected light intensity in the x direction, is the rate of change of the reflected light intensity in the y direction;

[0029] By calculating the light gradient of each pixel, the reflection unevenness of each local area is obtained. The calculation expression is as follows:

[0030]

[0031] , where L(x,y) is the measure of local light reflection inhomogeneity, θ is the sensitivity coefficient, is the nonlinear amplification factor;

[0032] After calculating the local reflection heterogeneity metric L(x,y) for each pixel, the local regions within the entire detection window are summarized to obtain the global reflection heterogeneity metric. The calculation expression is as follows:

[0033]

[0034] , where ω(x,y) is the pixel weight, is the gradient attenuation factor, γ is the attenuation coefficient, I reflect is the reference value of surface reflectance heterogeneity.

[0035] Preferably, after obtaining the surface texture differentiation reference value and the surface reflection heterogeneity reference value generated after analyzing the extracted key features, the surface texture differentiation reference value and the surface reflection heterogeneity reference value are input into a pre-learned deep learning model, and a coating consistency index is generated by the deep learning model, and the coating status of the plastic product surface is intelligently evaluated by the coating consistency index.

[0036] Preferably, the generated coating consistency index is compared and analyzed with a preset coating consistency index reference threshold value to classify the surface state of the plastic product. The classification steps are as follows:

[0037] If the coating consistency index is greater than or equal to a preset coating consistency index reference threshold, the current surface of the plastic product is in an uneven state and is classified as a heterogeneous coating;

[0038] If the coating consistency index is less than a preset coating consistency index reference threshold, the surface of the current plastic product is in a uniform state and is classified as a homogeneous coating.

[0039] Preferably, for plastic products that are judged to have heterogeneous coatings, the specific steps of dynamically adjusting the beam width and focal length of the scanner based on the evaluation results of the deep learning model are as follows:

[0040] If the plastic product being inspected is classified as having a heterogeneous coating, the scanner parameters are dynamically adjusted based on the model evaluation results to ensure that more details can be captured in areas with uneven coatings. Based on the evaluation results of the deep learning model, the dynamically adjusted beam width and focal length are refined and optimized based on the degree of coating unevenness. The calculation expression is as follows:

[0041] Beam_width_dynamic=f(CCI,Beam_width_init),

[0042] Focus_dynamic=f(CCI,Focus_init)

[0043] , where Beam_width_dynamic is the adjusted beam width, Beam_width_init is the initial beam width, Focus_dynamic is the adjusted focal length value, Focus_init is the initial focal length, and CCI is the coating consistency index;

[0044] Based on the evaluation of coating non-uniformity, the scanner captures surface condition information more precisely by adjusting the beam width and focal length. During this process, the adjustment range will depend on the actual surface coating characteristics and the degree of coating non-uniformity in the target area. The adjustment rule formula is as follows:

[0045] ΔBeam_width=A·(CCI ref -CCI),

[0046] ΔFocus=B·(CCIref-CCI)

[0047] , where ΔBeam_width is the adjustment amount of beam width, A is the beam width adjustment scale factor, CCIref is the coating consistency index reference threshold, ΔFocus is the adjustment amount of focal length, and B is the focal length adjustment scale factor;

[0048] To ensure optimal use of the adjusted parameters, the scanner dynamically adjusts the beam width and focal length as it captures new data. This is iteratively optimized based on preset rules and feedback mechanisms to ensure optimal image quality for each acquisition. The calculation expression is as follows:

[0049] Data_quality=f(Beam_width_dynamic,Focus_dynamic)

[0050] , where Data_quality is the data quality.

[0051] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0052] The present invention significantly improves the detection accuracy of uneven coatings on plastic products by introducing deep learning models and image analysis technology. In traditional scanner detection methods, due to the fixed light beam and unchanged focal length, the excessive reflection and light saturation problems caused by uneven coatings will affect the sensor's capture of details, resulting in distortion or loss of image data, and thus making it impossible to identify potential defects in a timely manner. However, this solution dynamically analyzes the surface texture differentiation reference value and the surface reflection heterogeneity reference value, two key features that can quantify the uniformity of the coating and the difference in reflected light. Through the intelligent evaluation of these features by the deep learning model, the model can accurately divide the coating status according to the coating consistency index, ensuring that in areas with uneven coatings, more detailed and professional analysis can be performed. This intelligent detection not only makes up for the limitations of traditional methods, but also can identify and mark potential coating defects in advance, ensuring that unqualified products do not enter the production process, thereby significantly reducing quality risks and reducing the possibility of product recalls.

[0053] For plastic products with uneven coatings, the present invention uses a deep learning model that can analyze the coating consistency index in real time and dynamically adjust the scanner's beam width and focal length based on the model evaluation results. This intelligent adjustment process means that when uneven coating is detected, the scanner can automatically reduce the beam width and adjust the focal length, thereby accurately capturing the details of the uneven coating. This strategy effectively avoids the drawbacks of traditional fixed parameter scanning, especially when faced with excessive reflection or saturated areas, and can ensure the integrity and high quality of image data with more appropriate optical settings. In addition, the intelligent beam and focal length adjustment strategy also improves the inspection efficiency of the production line, avoids unnecessary repeated inspections, and thus saves time and costs. Through this dynamic adjustment, the system can efficiently respond to the coating status of different plastic products, maintaining high precision and high speed while ensuring the accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0055] Figure 1 This is a module schematic diagram of a plastic product detection system based on image recognition according to the present invention. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0057] The present invention provides Figure 1 The plastic product inspection system shown in the figure, based on image recognition, includes an image acquisition module, a data storage and feature extraction module, a feature analysis and model input module, an intelligent evaluation and classification module, a homogeneous coating acquisition module, and a heterogeneous coating adjustment module:

[0058] The image acquisition module,first, uses a scanner to illuminate the surface of the plastic product with a preset beam width and focal length,,capturing high-quality surface images;

[0059] The scanner's preset beam width and focal length means that during the scanning process, the scanner's laser beam width and focal length are fixed using pre-set parameters. These parameters determine the beam size and focus sharpness when the scanner illuminates the plastic surface. The beam width affects the area covered by the scan, while the focal length determines the image clarity and detail captured. By presetting these parameters, the scanner can capture surface images with consistent accuracy and coverage with every scan, ensuring data reliability and consistency.

[0060] Data storage and feature extraction module, the collected surface images are stored and organized into a structured data set, and key features that help identify coating unevenness are extracted from the data set;

[0061] The collected surface images are stored and organized into a structured data set. First, the images need to be preprocessed, including denoising, standardization, image segmentation and other steps to ensure the consistency and high quality of the image data. Next, the processed images are organized in a certain format (such as a data table, matrix or feature vector) to form a data set for subsequent analysis. During the structuring process, the image data will be stored together with its corresponding labels (such as coating status) for subsequent model training and evaluation. The purpose of organizing into a structured data set is to facilitate subsequent feature extraction and analysis. Through standardized data structures, data can be efficiently screened, retrieved, and processed to ensure that the machine learning model can accurately extract valuable features from it and realize intelligent evaluation.

[0062] The feature analysis and model input module analyzes the extracted key features within the detection window and inputs the analyzed key features into a pre-learned deep learning model. The deep learning model then performs an intelligent assessment of the coating status on the surface of the plastic product.

[0063] Key features that help identify coating unevenness are extracted from the data set. The extracted features include texture changes and surface reflected light gradients. Under the detection window, the extracted texture changes and surface reflected light gradients are analyzed to generate surface texture differentiation reference values and surface reflection heterogeneity reference values, respectively. The surface texture differentiation reference values quantify the degree of variation in the spatial distribution and local areas of the surface texture of plastic products. The surface reflection heterogeneity reference values quantify the differences in reflected light intensity in different areas of the plastic product surface, which is specifically manifested as an uneven distribution of surface reflectivity.

[0064] Irregular changes in the surface texture of plastic products can usually indicate that the current plastic product's surface coating is uneven. The uniformity of the coating directly affects the texture distribution of the plastic surface, because factors such as the coating application method, thickness, and adhesion on the surface will cause different areas to exhibit different gloss, texture, and texture structure. When the coating is unevenly applied, some areas may appear too thick or too thin, resulting in significant changes in the surface smoothness, roughness, or texture direction in these areas. Specifically, uneven coating may form localized bumps or depressions on the surface, which in turn affects the reflection characteristics of light and causes irregular changes in the surface texture. For example, areas with thicker coating may exhibit a smooth, highly reflective texture, while areas with thinner coating may have a rougher surface texture. This difference in texture reflects problems with the thickness and uniformity of the coating. Therefore, texture irregularities are often a direct manifestation of coating unevenness, which can help the detection system identify coating quality problems and take appropriate measures.

[0065] In the detection window, the specific steps for analyzing texture changes and generating surface texture differentiation reference values are as follows:

[0066] In the detection window, texture features are first extracted from the image data of the plastic product surface. In order to quantify the changes in surface texture, the gray-level co-occurrence matrix (GLCM) is used to capture the changes in surface details. Texture features usually include contrast, homogeneity, energy, entropy, etc., which are used to represent the changes in grayscale values in the local area of the image. Through texture analysis of the local area, the texture pattern data of the area is extracted. These data reflect the differences in coating quality in different areas. Based on the extracted texture features, the texture difference of each area in the image is calculated. The calculation expression is as follows:

[0067]

[0068] , where is the value of the texture feature dimension of region i in the image at the kth dimension, is the value of the texture feature dimension of region j in the image at the kth dimension, α is the adjustment factor of the difference metric, which is used to control the contribution of the texture feature difference to the total difference metric, N is the total number of texture features, represents the difference between region i and region j in the kth texture feature dimension, D(t i ,t j ) is the texture difference, is the texture feature vector t i and t j Texture differences between

[0069] This step captures subtle texture changes by calculating the nonlinear difference in texture between local regions.

[0070] Based on the results of texture difference measurement, the texture differences of different regions in the image are calculated, and the differences are weighted by the weight factor. The calculation expression is as follows:

[0071]

[0072] , where Δ i is the regional texture difference, which indicates the texture difference of region i in the image, β j is the weight factor of the neighborhood area, is the weight factor of area j, and the weight factor is used to indicate the influence of neighborhood area j on the texture difference calculation of the current area i. Different weights can be assigned to different areas according to the relative importance of the neighborhood areas. is a set of neighborhood regions, is a set, representing the set of regions adjacent to region i;

[0073] This step further enhances the representation ability of local texture differences through weighting.

[0074] Combine all regional texture differences Δ i , generate the reference value of surface texture differentiation, the calculation expression is as follows:

[0075]

[0076] , where I texture is the reference value of surface texture differentiation, M is the total number of regions, λ i is the adjustment factor of region i, which adjusts the contribution of the texture difference of the region to the overall texture differentiation index;

[0077] Within the detection window, the larger the surface texture differentiation reference value generated after analyzing the texture changes, the more likely it is that the coating on the current plastic product surface is uneven. The surface texture differentiation index reflects the consistency of the coating on the plastic surface by quantifying the degree of variation in the spatial distribution and local areas of the texture. When the coating is uneven, irregular texture changes usually form on the surface, causing the texture characteristics of some areas to be significantly different from other areas. This irregular texture change will cause the differentiation index to increase. Conversely, when the coating is uniform, the surface texture changes less, the texture distribution of the entire surface tends to be consistent, and the differentiation index value is lower.

[0078] A rapid increase in the gradient of surface reflected light can usually indicate that the surface coating of a plastic product is uneven. The unevenness of the coating may cause an abnormal increase in the intensity of reflected light in certain areas of the surface, especially in areas where the coating is weak or unevenly distributed. This is because the optical properties of the coating (such as refractive index, reflectivity, etc.) will cause changes in the surface reflected light due to inconsistent thickness. In areas with uneven coating, when light contacts the surface, some areas may produce varying degrees of light reflection due to thin coating or partial loss, while the coating in other places may be thicker or uniform, and the reflected light is relatively stable. When the light gradient rises rapidly, it means that in some local areas, the intensity of the reflected light changes very drastically, which may be caused by uneven coating thickness, bubbles, cracks or other defects. This phenomenon indicates that the surface has uneven coating characteristics, which in turn affects image capture and subsequent quality inspection. Therefore, a rapidly rising reflected light gradient is an important indicator for evaluating the unevenness of the surface coating of plastic products.

[0079] In the detection window, the specific steps for analyzing the surface reflected light gradient and generating the surface reflectance heterogeneity reference value are as follows:

[0080] In the detection window, the reflected light data of the plastic product surface is collected by the scanner to obtain the reflected light intensity value of each sampling point. The light gradient is obtained by calculating the light intensity change of adjacent pixel points. The calculation expression is as follows:

[0081]

[0082] , where R(x,y) is the surface reflected light intensity value, which represents the reflected light intensity value at the image coordinate (x,y). It is the reflected light intensity gradient, which represents the reflected light intensity value of the reflected light intensity R(x,y) in the x and y directions, and is a measure of the local light intensity change. is the rate of change of the reflected light intensity in the x direction (horizontal direction), is the rate of change of the reflected light intensity in the y direction (vertical direction);

[0083] By calculating the reflected light gradient, the changes in light reflection in local areas of the surface can be revealed, providing a data basis for subsequent reflection heterogeneity analysis.

[0084] By calculating the light gradient of each pixel, the reflection unevenness of each local area is obtained. The calculation expression is as follows:

[0085]

[0086] , where L(x,y) is the local light reflection inhomogeneity measure, θ is the sensitivity coefficient, which is an adjustment coefficient used to control the sensitivity of the gradient to the inhomogeneity measure, is the nonlinear amplification coefficient, which controls the nonlinear degree of light gradient change;

[0087] This step exponentially amplifies the change in the reflection gradient, which can more accurately capture the uneven changes in the surface coating.

[0088] After calculating the local reflection heterogeneity metric L(x,y) for each pixel, the local regions within the entire detection window are summarized to obtain the global reflection heterogeneity metric. The calculation expression is as follows:

[0089]

[0090] , where ω(x,y) is the pixel weight, which is the weight of each pixel, indicating the importance of the pixel in calculating the reflectance heterogeneity metric. is the gradient attenuation factor, γ is the attenuation coefficient, which controls the influence of the light gradient on the reflectance heterogeneity metric, I reflect is the reference value of surface reflectance heterogeneity.

[0091] Under the detection window, the larger the surface reflection heterogeneity reference value, which is generated after analyzing the surface reflected light gradient, the more obvious the unevenness of the plastic product's surface coating is. When the coating on the plastic surface is uneven, the intensity and distribution of the reflected light in different areas will vary significantly. By analyzing the reflected light gradient and calculating the surface reflection heterogeneity index, it is possible to quantify this unevenness of the reflected light. Areas with larger surface reflected light gradients are usually caused by changes in coating thickness or uneven coating distribution, resulting in large spatial differences in light reflection. This difference is reflected as a higher value in the surface reflection heterogeneity index. Therefore, when the surface reflection heterogeneity index has a larger value, it indicates that there is an obvious uniformity problem on the surface of the coating; conversely, if the value of the index is low, it indicates that the coating on the surface of the plastic product is relatively uniform and the changes in reflected light are small.

[0092] After obtaining the surface texture differentiation reference value and surface reflection heterogeneity reference value generated by analyzing the extracted key features, the surface texture differentiation reference value and the surface reflection heterogeneity reference value are input into a pre-learned deep learning model, and a coating consistency index is generated by the deep learning model. The coating consistency index is used to intelligently evaluate the coating status of the plastic product surface.

[0093] The deep learning model is not limited here and can achieve the surface texture differentiation reference value I texture and surface reflectance heterogeneity reference value I reflect Any deep learning model that performs comprehensive analysis to generate the coating consistency index CCI can be used. To implement the technical solution of the present invention, the present invention provides a specific implementation method:

[0094] The coating consistency index CCI generation formula is as follows:

[0095]

[0096] , where b1 and b2 are the reference values of surface texture differentiation I texture and surface reflectance heterogeneity reference value I reflect The preset proportional coefficient, and b1 and b2 are both greater than 0.

[0097] The preset proportional coefficient refers to the parameter used to adjust the relative influence weight of different variables in the formula on the final coating consistency index CCI. In the formula, b1 and b2 are the surface texture differentiation reference values I texture and surface reflectance heterogeneity reference value I reflect The proportional coefficients of b1 and b2 are used to indicate the importance or contribution of these two parameters in the comprehensive generation of the coating consistency index. By adjusting these two coefficients, it is possible to flexibly adapt to different application scenarios or detection requirements. For example, if the change in surface texture during detection is more important for the coating consistency assessment, the value of b1 can be increased to give the surface texture differentiation reference value I texture The coating consistency index (CCI) places greater emphasis on the influence of texture features. The reasonable selection of preset proportional coefficients is optimized based on experimental data or actual experience to ensure that the formula can adapt to specific technical requirements and testing environments.

[0098] It can be seen from the coating consistency index that, under the detection window, the greater the performance value of the surface texture differentiation reference value generated after analyzing the texture change, and the greater the performance value of the surface reflection heterogeneity reference value generated after analyzing the surface reflected light gradient, the greater the performance value of the coating consistency index generated by comprehensive analysis of the extracted key features under the detection window, indicating that the coating on the surface of the current plastic product is uneven, otherwise, it indicates that the coating on the surface of the plastic product is relatively uniform.

[0099] A pre-learned deep learning model typically refers to a neural network model that has been trained on a large amount of labeled data and has had its weights and biases adjusted using a backpropagation algorithm. During training, this model learns from a large amount of labeled data (for example, surface image data of plastic products with known good and bad coating consistency) to extract effective features from this data and learn how to associate these features with the consistency of the surface coating. During training, the model continuously optimizes parameters (such as the weight matrix) to reduce the error between predicted and actual values until it can effectively handle new, similar data. Therefore, a pre-learned deep learning model already has the ability to automatically analyze and process input feature data. In practical applications, only new feature data needs to be input to make fast and efficient predictions.

[0100] For coating consistency testing, a pre-learned deep learning model will learn the complex relationship between texture changes and spectral absorption characteristics and coating consistency based on known annotated data. These relationships may be difficult to capture through manually set rules in traditional regularity models. Deep learning models, especially convolutional neural networks (CNN) and recurrent neural networks (RNN) models that have advantages in image processing and sequence data analysis, can automatically extract key features reflecting coating consistency from surface images and make intelligent assessments. The surface texture differentiation reference value and surface reflectance heterogeneity reference value input into the model are processed layer by layer by multiple neurons in the model to ultimately generate a coating consistency index, which quantifies the uniformity of the coating. The generation of the coating consistency index is the intelligent result obtained by the deep learning model after multiple training of the raw data. It can make efficient and accurate judgments on the coating status and provide support for subsequent production decisions or quality control.

[0101] Intelligent evaluation and classification module, which classifies the surface conditions of plastic products into homogeneous coating and heterogeneous coating based on the evaluation results of deep learning models;

[0102] The generated coating consistency index is compared with the pre-set coating consistency index reference threshold value to classify the surface conditions of the plastic products. The classification steps are as follows:

[0103] If the coating consistency index is greater than or equal to a preset coating consistency index reference threshold, the current surface of the plastic product is in an uneven state and is classified as a heterogeneous coating;

[0104] If the coating consistency index is less than a preset coating consistency index reference threshold, the surface of the current plastic product is in a uniform state and is classified as a homogeneous coating.

[0105] A homogeneous coating refers to a coating on the surface of a plastic product that has a high degree of consistency and uniformity, which is manifested in that the material is evenly distributed in terms of texture, thickness, color, gloss and reflective properties, without obvious regional differences or abnormal characteristics; a heterogeneous coating refers to a coating on the surface of a plastic product that has obvious non-uniformity or abnormal characteristics, which is manifested in that the material has significant changes in texture, thickness, color or gloss in local areas.

[0106] Homogeneous coating acquisition module: For plastic products that are judged to have homogeneous coatings, the scanner continues to collect surface state data with the original set beam width and focal length;

[0107] For plastic products judged to have a homogeneous coating, the original set beam width and focal length continue to be used to collect surface condition data, with the goal of maintaining high scanning efficiency when the coating is uniform. In this case, the coating consistency is good, and the scanner does not need to make complex beam width and focal length adjustments; data can be collected directly according to the initially set parameters. This not only avoids unnecessary parameter adjustments, saving time and computing resources during the scanning process, but also improves overall work efficiency. By maintaining constant scanning parameters, surface information of homogeneous coatings can be quickly and accurately acquired, providing reliable real-time data for subsequent quality monitoring and production processes, while reducing system burden and optimizing the operation of the entire production line.

[0108] The heterogeneous coating adjustment module dynamically adjusts the scanner's beam width and focal length based on the deep learning model's evaluation results for plastic products identified as having heterogeneous coatings.

[0109] For plastic products identified as having heterogeneous coatings, the scanner's beam width and focal length are dynamically adjusted based on the deep learning model's evaluation results. The specific steps are as follows:

[0110] If the plastic product being inspected is classified as having a heterogeneous coating, the scanner parameters, particularly the beam width and focal length, are dynamically adjusted based on the model evaluation results to ensure that more details can be captured in areas with uneven coatings. Based on the evaluation results of the deep learning model, the dynamically adjusted beam width and focal length are refined and optimized based on the degree of coating unevenness. The calculation expression is as follows:

[0111] Beam_width_dynamic=f(CCI,Beam_width_init),

[0112] Focus_dynamic=f(CCI,Focus_init)

[0113] , where Beam_width_dynamic is the adjusted beam width, which ensures that the scanner can finely capture image data of uneven coating areas when processing heterogeneous coatings, Beam_width_init is the initial beam width, Focus_dynamic is the adjusted focal length value, Focus_init is the initial focal length, and CCI is the coating consistency index;

[0114] Based on the evaluation of coating non-uniformity, the scanner adjusts the beam width and focal length to capture surface condition information more precisely. During this process, the extent of the adjustment will depend on the actual surface coating characteristics and the degree of coating non-uniformity in the target area. If the coating non-uniformity is high, the scanner's beam width may be reduced and the focal length may be increased to obtain more details and surface texture information. The adjustment rule formula is as follows:

[0115] ΔBeam_width=A·(CCI ref -CCI),

[0116] ΔFocus=B·(CCI ref -CCI)

[0117] , where ΔBeam_width is the adjustment amount of the beam width, that is, the beam width value that needs to be increased or decreased based on the original set beam width, A is the beam width adjustment proportional factor, which represents the strength of the relationship between the coating consistency index CCI difference and the beam width adjustment amount, CCI ref is the coating consistency index reference threshold, ΔFocus is the focus adjustment amount, that is, the focus value that needs to be changed to ensure that the scanner can clearly focus on the coating uneven area, and B is the focus adjustment scale factor, which indicates the strength of the relationship between the coating consistency index CCI difference and the focus adjustment amount;

[0118] Dynamic adjustments based on coating non-uniformity assessment help to accurately capture surface details and ensure high-quality scan data.

[0119] Calculating the adjusted beam width and focal length first, and then the adjustment amount, ensures that the scanner settings are optimized for coating non-uniformities. A deep learning model evaluates the results, determines the target beam width and focal length, and then calculates the adjustment amount, ensuring precise adaptation to varying coating conditions without affecting other acquisition parameters. This improves scan accuracy and makes adjustments more efficient.

[0120] To ensure optimal use of the adjusted parameters, the scanner dynamically adjusts the beam width and focal length as it captures new data. This is iteratively optimized based on preset rules and feedback mechanisms to ensure optimal image quality for each acquisition. The calculation expression is as follows:

[0121] Data_quality=f(Beam_width_dynamic,Focus_dynamic

[0122] , where Data_quality is the data quality, which is the final output variable and represents the quality of the image data obtained by dynamically adjusting the beam width and focal length.

[0123] By dynamically adjusting the scanner's beam width and focal length, the scanner recaptures image data from the plastic product's surface. This step aims to optimize scanning efficiency and data quality under these adjusted parameters, ensuring clear capture of details in uneven coating areas while avoiding the time and computational burden of excessive adjustments. These new scanning parameters, through intelligent control, ensure precise image acquisition while maintaining high production efficiency.

[0124] For plastic products identified as having heterogeneous coatings, after the deep learning model assesses the coating non-uniformity, the scanner dynamically adjusts its beam width and focal length based on the model's assessment. This step ensures sufficient detail and accurate data is captured in areas with non-uniform coatings to better identify surface defects such as bubbles, cracks, and scratches. Because areas with non-uniform coatings often exhibit significant visual variations in texture and reflection, a fixed beam width and focal length may not capture these details, requiring optimization based on the actual degree of coating non-uniformity.

[0125] Specifically, by dynamically adjusting the beam width, the scanner can focus more closely on local areas with uneven coatings, avoiding overexposure or light saturation, and ensuring that details in highly reflective areas remain clearly visible. Adjusting the focal length allows the scanner to maintain high image clarity at different surface heights and curvatures, thereby improving detection accuracy and reliability. Through this dynamic adjustment, the system can adapt to different coating conditions in real time, which not only improves detection sensitivity but also improves scanning efficiency, avoiding unnecessary repetitive operations and waste of resources. The implementation of this process can more accurately identify potential surface defects, ensure product quality, and reduce the flow of defective products into the market during the production process, thereby reducing quality risks and corporate losses.

[0126] The present invention significantly improves the detection accuracy of uneven coatings on plastic products by introducing deep learning models and image analysis technology. In traditional scanner detection methods, due to the fixed light beam and unchanged focal length, the excessive reflection and light saturation problems caused by uneven coatings will affect the sensor's capture of details, resulting in distortion or loss of image data, and thus making it impossible to identify potential defects in a timely manner. However, this solution dynamically analyzes the surface texture differentiation reference value and the surface reflection heterogeneity reference value, two key features that can quantify the uniformity of the coating and the difference in reflected light. Through the intelligent evaluation of these features by the deep learning model, the model can accurately divide the coating status according to the coating consistency index, ensuring that in areas with uneven coatings, more detailed and professional analysis can be performed. This intelligent detection not only makes up for the limitations of traditional methods, but also can identify and mark potential coating defects in advance, ensuring that unqualified products do not enter the production process, thereby significantly reducing quality risks and reducing the possibility of product recalls.

[0127] For plastic products with uneven coatings, the present invention uses a deep learning model that can analyze the coating consistency index in real time and dynamically adjust the scanner's beam width and focal length based on the model evaluation results. This intelligent adjustment process means that when uneven coating is detected, the scanner can automatically reduce the beam width and adjust the focal length, thereby accurately capturing the details of the uneven coating. This strategy effectively avoids the drawbacks of traditional fixed parameter scanning, especially when faced with excessive reflection or saturated areas, and can ensure the integrity and high quality of image data with more appropriate optical settings. In addition, the intelligent beam and focal length adjustment strategy also improves the inspection efficiency of the production line, avoids unnecessary repeated inspections, and thus saves time and costs. Through this dynamic adjustment, the system can efficiently respond to the coating status of different plastic products, maintaining high precision and high speed while ensuring the accuracy of detection.

[0128] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A plastic product detection system based on image recognition, characterized in that: It includes image acquisition module, data storage and feature extraction module, feature analysis and model input module, intelligent evaluation and classification module, homogeneous coating acquisition module and heterogeneous coating adjustment module: The image acquisition module,first, uses a scanner to illuminate the surface of the plastic product with a preset beam width and focal length,,capturing high-quality surface images; Data storage and feature extraction module, the collected surface images are stored and organized into a structured data set, and key features that help identify coating unevenness are extracted from the data set; The feature analysis and model input module analyzes the extracted key features within the detection window and inputs the analyzed key features into a pre-learned deep learning model. The deep learning model then performs an intelligent assessment of the coating status on the surface of the plastic product. Intelligent evaluation and classification module, which classifies the surface conditions of plastic products into homogeneous coating and heterogeneous coating based on the evaluation results of deep learning models; Homogeneous coating acquisition module: For plastic products that are judged to have homogeneous coatings, the scanner continues to collect surface state data with the original set beam width and focal length; The heterogeneous coating adjustment module dynamically adjusts the scanner's beam width and focal length for plastic products judged to have heterogeneous coatings based on the deep learning model evaluation results.

2. The plastic product detection system based on image recognition according to claim 1, characterized in that: Key features that help identify coating unevenness are extracted from the data set. The extracted features include texture changes and surface reflected light gradients. Under the detection window, the extracted texture changes and surface reflected light gradients are analyzed to generate surface texture differentiation reference values and surface reflection heterogeneity reference values, respectively. The surface texture differentiation reference values quantify the degree of variation in the spatial distribution and local areas of the surface texture of plastic products. The surface reflection heterogeneity reference values quantify the differences in reflected light intensity in different areas of the plastic product surface, which is specifically manifested as an uneven distribution of surface reflectivity.

3. The plastic product detection system based on image recognition according to claim 2, characterized in that: In the detection window, the specific steps for analyzing texture changes and generating surface texture differentiation reference values are as follows: In the detection window, texture features are first extracted from the image data of the plastic product surface. Texture pattern data of the local area is extracted through texture analysis. Based on the extracted texture features, the texture difference of each area in the image is calculated. The calculation expression is as follows: Where, is the value of the texture feature dimension of region i in the image at the kth dimension, is the value of the texture feature dimension of region j in the image, α is the adjustment factor of the difference metric, N is the total number of texture features, represents the difference between region i and region j in the kth texture feature dimension, D(t i ,t j ) is the texture difference; Based on the results of texture difference measurement, the texture differences of different regions in the image are calculated, and the differences are weighted by the weight factor. The calculation expression is as follows: Where, Δ i is the regional texture difference, β j is the weight factor of the neighborhood region, is the weight factor of region j, is a set of neighborhood regions, is a set, representing the set of regions adjacent to region i; Combine all regional texture differences Δ i , generate the reference value of surface texture differentiation, the calculation expression is as follows: Where, I texture is the reference value of surface texture differentiation, M is the total number of regions, λ i is the adjustment factor for region i.

4. The plastic product detection system based on image recognition according to claim 2, characterized in that: In the detection window, the specific steps for analyzing the surface reflected light gradient and generating the surface reflectance heterogeneity reference value are as follows: In the detection window, the reflected light data of the plastic product surface is collected by the scanner to obtain the reflected light intensity value of each sampling point. The light gradient is obtained by calculating the light intensity change of adjacent pixel points. The calculation expression is as follows: Where R(x,y) is the intensity of the reflected light on the surface, is the reflected light intensity gradient, is the rate of change of the reflected light intensity in the x direction, is the rate of change of the reflected light intensity in the y direction; By calculating the light gradient of each pixel, the reflection unevenness of each local area is obtained. The calculation expression is as follows: Where L(x,y) is the measure of local light reflection non-uniformity, θ is the sensitivity coefficient, is the nonlinear amplification factor; After calculating the local reflection heterogeneity metric L(x,y) for each pixel, the local regions within the entire detection window are summarized to obtain the global reflection heterogeneity metric. The calculation expression is as follows: Where ω(x,y) is the pixel weight, is the gradient attenuation factor, γ is the attenuation coefficient, I reflect is the reference value of surface reflectance heterogeneity.

5. The plastic product detection system based on image recognition according to claim 2, characterized in that: After obtaining the surface texture differentiation reference value and surface reflection heterogeneity reference value generated by analyzing the extracted key features, the surface texture differentiation reference value and the surface reflection heterogeneity reference value are input into a pre-learned deep learning model, and a coating consistency index is generated by the deep learning model. The coating consistency index is used to intelligently evaluate the coating status of the plastic product surface.

6. The plastic product detection system based on image recognition according to claim 5, characterized in that: The generated coating consistency index is compared with the pre-set coating consistency index reference threshold value to classify the surface conditions of the plastic products. The classification steps are as follows: If the coating consistency index is greater than or equal to a preset coating consistency index reference threshold, the current surface of the plastic product is in an uneven state and is classified as a heterogeneous coating; If the coating consistency index is less than a preset coating consistency index reference threshold, the surface of the current plastic product is in a uniform state and is classified as a homogeneous coating.

7. The plastic product detection system based on image recognition according to claim 6, characterized in that: For plastic products identified as having heterogeneous coatings, the scanner's beam width and focal length are dynamically adjusted based on the deep learning model's evaluation results. The specific steps are as follows: If the plastic product being inspected is classified as having a heterogeneous coating, the scanner parameters are dynamically adjusted based on the model evaluation results to ensure that more details can be captured in areas with uneven coatings. Based on the evaluation results of the deep learning model, the dynamically adjusted beam width and focal length are refined and optimized based on the degree of coating unevenness. The calculation expression is as follows: Beam_width_dynamic=f(CCI,Beam_width_init) Focus_dynamic=f(CCI,Focus_init) Where Beam_width_dynamic is the adjusted beam width, Beam_width_init is the initial beam width, Focus_dynamic is the adjusted focal length value, Focus_init is the initial focal length, and CCI is the coating consistency index; Based on the evaluation of coating non-uniformity, the scanner captures surface condition information more precisely by adjusting the beam width and focal length. During this process, the adjustment range will depend on the actual surface coating characteristics and the degree of coating non-uniformity in the target area. The adjustment rule formula is as follows: ΔBeam_width=A·(CCI ref -CCI) ΔFocus=B·(CCI ref -CCI) Where ΔBeam_width is the adjustment amount of the beam width, A is the beam width adjustment proportional factor, and CCI ref is the coating consistency index reference threshold, ΔFocus is the focal length adjustment amount, and B is the focal length adjustment scale factor; To ensure optimal use of the adjusted parameters, the scanner dynamically adjusts the beam width and focal length as it captures new data. This is iteratively optimized based on preset rules and feedback mechanisms to ensure optimal image quality for each acquisition. The calculation expression is as follows: Data_quality=f(Beam_width_dynamic,Focus_dynamic) Where Data_quality is the data quality.

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