Contraband Identification Algorithm for Intelligent Millimeter-wave Security Screening Device based on Regional-convolution Neural Network Algorithm for Civil Aviation

다국어 초록 (Multilingual Abstract)

Security is directly tied to the protection of public property and lives and is one of the key safeguards for the regular functioning of civil aviation. Millimeter-wave-based security screening technology has been developed to handle the demand for security screening during periods of high passenger traffic and to minimize the involvement of security personnel. But it cannot meet the need for quick passage at peak passenger flow because the present millimeter-wave contraband image content is insufficient, and the target-detection accuracy is low. In order to solve this problem, this study examined the denoising of millimeter-wave contraband images based on mean filtering and the wavelet transform, and the Canny algorithm was used to realize the edge detection of images. A mask region-convolutional neural network algorithm was used to identify contraband targets in the detection area to realize the real-time monitoring of millimeter-wave security equipment. The peak signal-to-noise ratio and mean square error of the mean filtered denoising-wavelet transform algorithm were 25.439 dB and 65.4781, respectively. The classification accuracy rates were greater than those of the fast area convolutional neural network model-based approach (93.65%, 89.94%, and 91.25%, respectively). In conclusion, the suggested algorithm is reliable and effective at locating and identifying targets for contraband in civil aviation security screening.