In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and w...In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.展开更多
The integration of hyperspectral imaging and deep learning for foreign fiber detection has primarily focused on plastic film.However,detecting various foreign fibers in long-staple cotton,particularly those that are w...The integration of hyperspectral imaging and deep learning for foreign fiber detection has primarily focused on plastic film.However,detecting various foreign fibers in long-staple cotton,particularly those that are white,transparent or similar in color,remains a significant challenge.The spectral response differences of various foreign fibers across different wavelengths are significant,which makes hyperspectral multi-target detection more complex.To address this challenge,a hyperspectral identification algorithm is proposed.First,hyperspectral image of the experimental samples are captured,and principal component analysis(PCA)is applied to select the optimal feature bands for recognition by a convolutional neural network.Next,the AlexNet model is fine-tuned to optimal parameters using the primary feature bands.After multidimensional experimental validation,the PCA-AlexNet model efficiently identifies foreign fibers.Finally,after analyzing the experimental results from multiple perspectives,the fiber identification model is identified as PCA-AlexNet-23.The results show that the PCA-AlexNet-23 model excels in identifying multiple fibers,achieving an overall accuracy of 97.2%,an average accuracy of 95.2%,and a Kappa coefficient of 93.1%.These accuracy rates outperform those of a support vector machine,artificial neural network,LDA-VGGNet and LDA-LeNet models.In the experimental tests,the overall foreign fiber removal rate exceeds 85%.展开更多
Since foreign fibers in cotton seriously affect the quality of the final cotton textile products,machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equi...Since foreign fibers in cotton seriously affect the quality of the final cotton textile products,machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equipment.As one of the key components in detection systems,the suitable and good classifier is significantly important for machine-vision-based on detection systems for foreign fibers in cotton due to it improving the system’s performance.In the study,we test five classifiers in the dataset of foreign fibers in cotton,and for finding the best feature set corresponding to the classifiers,we use the four filter feature selection approaches to find the best feature sets of foreign fibers in cotton corresponding to specific classifiers.The experimental results show that the extreme learning machine and kernel support vector machines have the excellent performance for foreign fiber detection and the classification accuracy are respectively 93.61%and 93.17%using the selected corresponding feature set with 42 and 52 features.展开更多
Cotton bales are frequently intermingled with foreign fibers which will interfere in the process of spinning,weaving and dyeing and will worsen the product quality.Nowadays,cotton fibers are sorted manually in most of...Cotton bales are frequently intermingled with foreign fibers which will interfere in the process of spinning,weaving and dyeing and will worsen the product quality.Nowadays,cotton fibers are sorted manually in most of the cotton textile mills with very low efficiency.There is a great demand for foreign fiber detection devices in Chinese cotton textile mills.The air flow in the conveying pipe of the device has an important effect on the image acquisition,image analysis and removal of foreign fibers.As a primary effort,the air flow in the conveying pipe of the foreign fiber detection device was simulated numerically.The effects of the inlet air velocity on the air turbulence intensity and air velocity along the detecting section were studied.展开更多
In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated ...In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber identification.This paper presents a new approach for the fast processing of foreign fiber images.This approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image connection.At first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several blocks.Thereafter,the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision.Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening.Finally,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image.The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods.On the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.展开更多
文摘In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.
基金the Key Research and Development Projects of the Xinjiang Uygur Autonomous Region,China(Research on Key Technologies of Automatic Recognition of Foreign Fibers in Machine-picked Long-Staple Cotton)the 2023 Shandong College Students for Innovation and Entrepreneurship Training Program,China (S202310447117)。
文摘The integration of hyperspectral imaging and deep learning for foreign fiber detection has primarily focused on plastic film.However,detecting various foreign fibers in long-staple cotton,particularly those that are white,transparent or similar in color,remains a significant challenge.The spectral response differences of various foreign fibers across different wavelengths are significant,which makes hyperspectral multi-target detection more complex.To address this challenge,a hyperspectral identification algorithm is proposed.First,hyperspectral image of the experimental samples are captured,and principal component analysis(PCA)is applied to select the optimal feature bands for recognition by a convolutional neural network.Next,the AlexNet model is fine-tuned to optimal parameters using the primary feature bands.After multidimensional experimental validation,the PCA-AlexNet model efficiently identifies foreign fibers.Finally,after analyzing the experimental results from multiple perspectives,the fiber identification model is identified as PCA-AlexNet-23.The results show that the PCA-AlexNet-23 model excels in identifying multiple fibers,achieving an overall accuracy of 97.2%,an average accuracy of 95.2%,and a Kappa coefficient of 93.1%.These accuracy rates outperform those of a support vector machine,artificial neural network,LDA-VGGNet and LDA-LeNet models.In the experimental tests,the overall foreign fiber removal rate exceeds 85%.
基金The authors would like to thank the Guangdong Natural Science Foundation(No.2016A030310072)Special Innovation Project of Guangdong Education Department(No.2017GKTSCX063)+1 种基金MOE(Ministry of Education in China)Project of Humanities and Social Sciences(Nos.17YJCZH261 and 17YJCZH157)Project of Shenzhen Education Science Planning(No.ybfz16009)for their financial support.
文摘Since foreign fibers in cotton seriously affect the quality of the final cotton textile products,machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equipment.As one of the key components in detection systems,the suitable and good classifier is significantly important for machine-vision-based on detection systems for foreign fibers in cotton due to it improving the system’s performance.In the study,we test five classifiers in the dataset of foreign fibers in cotton,and for finding the best feature set corresponding to the classifiers,we use the four filter feature selection approaches to find the best feature sets of foreign fibers in cotton corresponding to specific classifiers.The experimental results show that the extreme learning machine and kernel support vector machines have the excellent performance for foreign fiber detection and the classification accuracy are respectively 93.61%and 93.17%using the selected corresponding feature set with 42 and 52 features.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of Chinasupported by the Fok Ying Tung Education Foundation for University Youngsters+1 种基金supported by the National Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu province(BK2009123).
文摘Cotton bales are frequently intermingled with foreign fibers which will interfere in the process of spinning,weaving and dyeing and will worsen the product quality.Nowadays,cotton fibers are sorted manually in most of the cotton textile mills with very low efficiency.There is a great demand for foreign fiber detection devices in Chinese cotton textile mills.The air flow in the conveying pipe of the device has an important effect on the image acquisition,image analysis and removal of foreign fibers.As a primary effort,the air flow in the conveying pipe of the foreign fiber detection device was simulated numerically.The effects of the inlet air velocity on the air turbulence intensity and air velocity along the detecting section were studied.
基金The authors thank National Natural Science Foundation of China(30971693,61170039)Ministry of Education of People’s Republic of China(NCET-09-0731)+2 种基金Hebei Education Department(Q2012063)Hebei University(2010-207)Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education(X11-01),for their financial support.
文摘In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products.As the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber identification.This paper presents a new approach for the fast processing of foreign fiber images.This approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image connection.At first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several blocks.Thereafter,the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision.Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening.Finally,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image.The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods.On the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.
