In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.

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Aiming at the complex surface condition of the printing roller and the requirement of high precision and high efficiency of the surface defect detection of the printing roller, a detection algorithm based on visual salience is proposed. Firstly, the dodging processing is used to eliminate the uneven illumination of the printing roller image; secondly, the non local means denoising algorithm is used to weaken the surface texture based on the redundant information commonly existing in the printing roller image; secondly, the spectral residual salience algorithm is used to calculate the salience of defects in the image and obtain the saliency map; finally, Sobel is used to detect the saliency map of defects and the manually labeled The defect images were compared and analyzed. Experimental results show that the algorithm has high recognition rate and accuracy, and can meet the needs of surface defect detection of printing roller.

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