This paper proposes a visual recognition and localization method based on color classification by K-means and OTSU. Based on the characteristics of HSV color space which is convenient for color segmentation, after transferring the image from RGB color space to HSV color space, the K-means algorithm is combined with the difference method to segment the targets with the same color characteristics. For the targets with the same color characteristics, the OTSU algorithm is considered to segment the top surface of the target in the S-channel of the image, and the center of the top surface of the target is calculated using the image processing method as the suction point of the robotic handling. The experimental results show that the method is characterized by high accuracy, robustness and real-time performance in experiments such as target recognition based on color classification.
The rail bolt joint is an important component of railway tracks, which ensure operational safety and vehicle comfort. However, existing non-destructive testing methods fail to accurately assess the severity of the rail bolt joint looseness. A detection method based on Vibration-acoustic modulation was proposed to detect early loosening of the rail bolt joint. First, A single degree of freedom model was established based on the relationship between bolt torque and equivalent stiffness. Then, the looseness index was constructed using the fundamental wave and sideband amplitude. Finally, a rail bolt loosening detection experiment was carried out. Experimental studies showed that the degree of bolt loosening had a significant effect on the frequency amplitude characteristics of the modulating signal. Moreover, the bolt loosening index exhibited staged changed corresponding to increases in bolt prestress, which can be used to identify early loosening of the rail bolt joint.
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