The rail transportation industry is moving towards heavy loads, high speeds as well as high density operation, which places higher demands on the quality of heavy rail. The quality inspection of rail defects using machine vision first requires obtaining clear surface/planar images of multiple surfaces of the hot heavy rail. Therefore, a vision inspection solution is proposed, which uses a 6-lane linear CCD to photograph the tread surface, bottom surface, and upper and lower waist surfaces of the heavy rail. A comprehensive comparison of various types of light sources was conducted through experiments on chromaticity, brightness, and spectral power. To solve the problem of image overexposure due to infrared radiation of the hot heavy rail, the spectral radiation characteristics of the hot heavy rail were analyzed, and the imaging effects of adding different filters were compared and analyzed. Finally, to solve the problem of out-of-focus due to oscillation of the heavy rail during rolling, the image is analyzed by an automatic focus search strategy, and the results are fed back to the camera to achieve automatic focus, thus adaptively obtaining good image quality and laying a good foundation for further defects detection.
To reduce the noise amplification and ripple phenomenon in the restoration result by using the traditional Richardson-Lucy deconvolution method, a novel hybrid regularization image restoration algorithm based on total variation is proposed in this paper. The key ides is that the hybrid regularization terms are employed according to the characteristics of different regions in the image itself. At the same time, the threshold between the different regularization terms is selected according to the golden section point which takes into account the human eye's visual feeling. Experimental results show that the restoration results of the proposed method are better than that of the total variation Richardson-Lucy algorithm both in PSNR and MSE, and it has the better visual effect simultaneously.
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