Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
In order to solve the problems of low detection efficiency and safety of artificial surface defects in hot-state cross wedge rolling shaft production line, a machine vision-based method for detecting surface hollow defect of hot-state shafts is proposed. Firstly, by analyzing the high reflective properties of the metal shaft surface, the best lighting method was obtained. And by analyzing the image contrast between image foreground and image background, the most suitable optical filter type in image acquisition was determined. Then, Fourier Gaussian low-pass filtering method is used to remove the interference noise of rolled shafts surface in frequency domain, such as high-light, oxide skin and surface texture. Finally, by analyzing the characteristics of the surface hollow defect area, a defect identification method combining the Otsu threshold method and the adaptive threshold method is proposed to realize the effective extraction of surface hollow defect of rolled shafts. The test results show that the average recognition rate of the method based on machine vision is 95.7%. The results of this paper provide technical support to meet the production requirements of high quality and high performance of cross wedge rolling....
In view of the need to remove empty cells and unqualified seedlings for automatic transplanting of leafy vegetable seedlings, this paper proposes a method to detect the growth parameters of leafy vegetable seedlings by using machine vision technology. This method uses the image processor PV200 to perform image grayscale, threshold segmentation, corrosion, expansion, area division, etc. to obtain the pixel value of the leaf area of the seedling and compare it with the set standard value, which provides guiding information for eliminating empty cells and unqualified seedlings. Lettuce seedlings at 17 days, 20 days, and 22 days of seedling age were used as the test objects, and the growth status and test results of the seedlings were analyzed to determine the optimum seedling age for transplanting. The test results show that there is basically no leaf cross-border between the lettuce seedlings at the age of 17 days, the average pixel area of the leaves is 3771.74, and the detection accuracy rate is 100%; the seedlings at the age of 22 days grow 5–6 leaves, the detection accuracy of unqualified seedlings and qualified seedlings was 62.50% and 88.16%, respectively, and the comprehensive detection accuracy was 85.71%. The comprehensive detection accuracy rate showed a downward trend with the increase of seedling age, mainly due to the partial occlusion between leaves. The transplanting of leafy vegetable seedlings is a sparse transplanting operation, and the seedling spacing increases after transplanting. Therefore, the detection of seedlings in the process of transplanting can greatly improve the recognition accuracy and solve the problem that the leaves of the seedlings in the seedling tray are obscured by each other and affect the detection accuracy. The research results can provide a theoretical basis and design reference for the development of the visual inspection system and the transplanting actuator of the leafy vegetable seedlings transplanting robot....
To realize the real-time automatic identification of adulterated minced mutton, a convolutional neural network (CNN) image recognition model of adulterated minced mutton was constructed. Images of mutton, duck, pork and chicken meat pieces, as well as prepared mutton adulterated with different proportions of duck, pork and chicken meat samples, were acquired by the laboratory’s self-built image acquisition system. Among all images were 960 images of different animal species and 1200 images of minced mutton adulterated with duck, pork and chicken. Additionally, 300 images of pure mutton and mutton adulterated with duck, pork and chicken were reacquired again for external validation. This study compared and analyzed the modeling effectiveness of six CNN models, AlexNet, GoogLeNet, ResNet-18, DarkNet-19, SqueezeNet and VGG-16, for different livestock and poultry meat pieces and adulterated mutton shape feature recognition. The results show that ResNet-18, GoogLeNet and DarkNet-19 models have the best learning effect and can identify different livestock and poultry meat pieces and adulterated minced mutton images more accurately, and the training accuracy of all three models reached more than 94%, among which the external validation accuracy of the optimal three models for adulterated minced mutton images reached more than 70%. Image learning based on a deep convolutional neural network (DCNN) model can identify different livestock meat pieces and adulterated mutton, providing technical support for the rapid and nondestructive identification of mutton authenticity....
This paper presents the application of machine vision and learning techniques to detect and identify the number of flower clusters on apple trees leading to the ability to predict the potential yield of apples. A new field robot was designed and built to collect and build a dataset of 1500 images of apples trees. The trained model produced a cluster precision of 0.88 or 88% and a percentage error of 14% over 106 trees running the mobile vehicle on both sides of the trees. The detection model was predicting less than the actual amount but the fruit flower count is still significant in that it can give the researcher information on the estimated growth and production of each tree with respect to the actions applied to each fruit tree. A bias could be included to compensate for the average undercount. The resulting F1-Score of the object detection model was 80%, which is similar to other research methods ranging from an F1-Score of 77.3% to 84.1%. This paper helps lay the foundation for future application of machine vision and learning techniques within apple orchards or other fruit tree settings....
Seed processing is an important means of improving seed quality. However, the traditional seed processing process and parameter adjustment are highly empirically dependent. In this study, machine vision technology was used to develop a seed processing method based on the rapid extraction of seeds’ material characteristics. Combined with the results of clarity analysis and the single seed germination test, the seed processing process and parameters were determined through data analysis. The results showed that several phenotypic features were significantly or highly significantly correlated with clarity, but fewer phenotypic features were correlated with viability. According to the probability density distribution of pure seeds and impurities in the features that were significantly correlated with seed clarity, the sorting parameters of length, width, R, G, and B were determined. When the combination of width (≥0.8 mm) + G (<75) was used for sorting, the recall of pure seeds was higher than 91%, and the precision was increased to 98.6%. Combined with the specific production reality, the preliminary determination of the Platycodon grandiflorum seed processing process was air separation—screen (round hole sieve)—color sorting. Then, four commercialized Platycodon grandiflorum seed lots were sorted by this process using corresponding parameters in the actual processing equipment. Subsequently, the seed clarity and germination percentage were significantly improved, and the seed quality qualification rate was increased from 25% to 75%. In summary, by using machine vision technology to quickly extract the material characteristics of the seeds, combined with correlation analysis, probability density distribution plots, single feature selection, and combination sorting comparisons, the appropriate processing process and corresponding sorting parameters for a specific seed lot can be determined, thus maximizing the seed quality....
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