The existing fire alarm system has strict distance and installation requirements between the fire point and the detector, and is easy to be interfered by environmental factors. It is not suitable for places with large space and many interference factors such as Climbazole production line. This paper proposed a flame image detection technology based on RGB+HSI color model and the detection system is designed and developed. The experimental results show that the flame image detection system based on RGB+HSI color model has the better recognition efficiency, which meets the real-time and accuracy requirements for early flame image detection in Climbazole production line.
Non-destructive testing technology for large grinding wheel geometry is getting more and more attention from the industry. A device based on machine vision technology for intelligent measurement of large grinding wheel size is introduced. After calibrating and measuring the inside and outside radius of the grinding wheel and the thickness of the grinding wheel, intelligent detection is realized through a series of operations such as binarization of the original map, filling, expanding, outline extraction and outline coordinate extraction through hardware design and software programming. The hardware requirements of this design are simple. When measuring the radius of a grinding wheel, the method described in this paper gives the results of radius and height measurements with accuracy up to 5mm and 1mm, respectively. Finally, through repeated measurement experiments, the intelligent detection device of large grinding wheel size established in this paper can effectively solve the problems of field calibration of large grinding wheel and fast detection of inside and outside diameters.
The train wheelset is a crucial part of railway vehicles, and its damage may lead to serious safety accidents. Therefore, it is imperative to detect tread damage timely and accurately. With the rapid development of deep learning, the image detection method based on a convolutional neural network (CNN) has played an important role. Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the target detection field. The algorithm has achieved excellent results in target detection, but there is a low recognition rate for small targets. Therefore, we propose an improved SSD target detection algorithm. The Original SSD algorithm is ineffective in detecting small targets with pits and cracks, so conv3-3 is selected to join the detection. We optimize convolution kernel parameters; the convolution layer contains more small target details. Compared with the original SSD, the Mean Average Precision (MAP) of tread defect is improved by 4.38%, and the MAP of small target detection is enhanced by 7.24%. This algorithm has a better performance in detection accuracy.
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.
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