Foreign fibers in cotton have serious adverse effects on the quality of textile products, so its effective identification and elimination has important significance and social value. To solve the above problems, we propose a fusion image pretreatment method based on limited contrast adaptive histogram equalization ( CLAHE ) and wavelet analysis ( WT ), The collected cotton polarization images were processed by WT & CLAHE, which effectively improved the contrast of anisotropic fibers in cotton images, and laid the foundation for the rapid and accurate identification of various anisotropic fibers in cotton in the later stage, It laid a foundation for the rapid and accurate identification of all kinds of anisotropic fibers in cotton in the later stage. Compared with manual and systematic detection, the results showed that technical personnel and detection system could accurately detect and identify dead leaves, white paper and color paper without interference from external environment and foreign fiber size. For white wool, hair and mulch film due to similar color or shape is small, technical personnel in the detection is easy to miss, and the detection system in WT & CLAHE image pretreatment, white wool, hair and mulch film detection accuracy is obviously due to artificial detection, especially for the mulch film this is not easy to detect foreign fiber has good recognition effect.
With rapid development of rail transport in our country, more and more people choose because of on time, fast and convenient. Safety of the subway is urgent with passenger increasing, and it's very important to inspire hidden danger. The paper proposed The auto-inspection method based on Infrared Laser Imaging and Deep Learning to detect foreign objects between subway doors and the platform screen doors(PSDs). Fast-RCNN Algorithm based on TensorFlow Deep Learning frame was adopted and the image information were fused with classification model, vgg16. The detecting system was built and experiments were made and analyzed. The experimental results showed that this system and method was robust to The illumination variations and focussing. The system is simple and cost-effective and The algorithm is promising for detecting accuracy. The method and technology can be potentially applied for The subway safety detection.
Rolling quality is a key index of the cotton quality, which directly influences the quality of the lint and textiles, however, it is mainly decided through visual classification by skilled personnel. In order to realize the intelligent rapid classification of cotton quality, this paper proposed a decision-level fusion recognition method for the cotton quality grade based on colored-image information. After the preprocessing of images, RGB and HSV features were calculated, respectively. The features are normalization processed and principal component analysis (PCA) is employed to extract the greater contribution features of RGB and HSV images, which are adopted as BP neural network (BPNN) input parameters to identify the quality grade recognition of cotton, respectively, and then output parameters of BPNN are used as independent evidence to construct Basic Probability Assignment (BPA). Finally, D-S Theory is used to obtain the decision fusion and realize the high accuracy the recognition of cotton quality grades. The compared experimental results show that the precision of proposed method is significantly superior to classification using RGB and HSV features respectively. The method provided in this paper can realize the intelligent rapid classification of cotton quality, and proves the feasibility of cotton-graded artificial intelligent classification.
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