Abstract
In order to solve the problem of poor image recognition accuracy of edge devices in the dark environment, the author simulate the change of brightness and contrast in the dark environment by using matlab's internal function on the image through the point operation method in matlab, and add Salt & pepper and Gaussian noise to simulate the noise generated by the image in the dark environment. The modified images are imported into the image recognition system trained by migration learning to compare the changes in recognition accuracy and to investigate the main factors affecting the image recognition accuracy in low light conditions. Meanwhile, the main factors affecting the image recognition accuracy are improved by median filtering and Wiener filtering to find the image enhancement method that is most beneficial to improve the image recognition accuracy of edge devices in the dark light environment. The experimental results show that the main factor affecting the image recognition accuracy in the dark environment is the Salt & pepper noise, and the median filtering can remove the Salt & pepper noise well and improve the recognition accuracy of a single image up to 70%.
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