Mr. Shiheng Yan Profile

Shiheng Yan

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Proceedings Article | 5 November 2020 Paper

An auxiliary intelligent identification system for contraband of x-ray machine

Demao Ye, Zhiyuan Li, Yuhui Wang, Shiheng Yan

Proceedings Volume 11565, 115650Y (2020) https://doi.org/10.1117/12.2579877

KEYWORDS: Inspection, X-rays, Image enhancement, Intelligence systems, Image restoration, X-ray imaging, Safety

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In the international environment of violence, terrorist attacks and illegal drug smuggling, security issues are particularly important. Many companies and institutions in the world have successively developed safety inspection equipment, among which x-ray safety inspection equipment has been widely used. In order to ensure the safety of passengers, it is necessary to be able to accurately identify the knives, guns, inflammables, explosives and other dangerous articles in the luggage package during the security check, so as to reduce the probability of danger. At present, airport security inspectors need to change for half an hour, which is hard to work and easy to miss. An assistant identification system of contraband based on yolov4 to assist the security inspector to judge the X-ray image was proposed to improve the efficiency and accuracy of security inspection identification, reduce the manual detection intervention of professional training personnel, and avoid the occurrence of missed detection and false detection. A novel data augmentation method is proposed to guarantee the performance of the system in the case of a small number of samples. The experimental results show that the system has strong robustness in different channel directions and complex scenes, the comprehensive detection rate of lighter is higher than 95%, and the recognition efficiency and accuracy are greatly improved compared with the traditional convolutional neural network(CNN).

Proceedings Article | 24 October 2017 Paper

A novel algorithm for maneuvering target detection under the high energy laser irradiating

Demao Ye, Jing Wang, Peizheng Li, Shiheng Yan

Proceedings Volume 10462, 104624E (2017) https://doi.org/10.1117/12.2285535

KEYWORDS: Detection and tracking algorithms, Target detection, Laser energy, Motion estimation

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The high-energy laser weapon is famous for its unique advantage of speed-of-light response which was considered as an ideal weapon against Unmanned Aerial Vehicle(UAV). However, due to the high energy laser reflection effect, the pixel gray distribution of the frame image will be changed drastically, and therefore the miss distance signal will be interfered strongly when the high energy laser irradiating on the UAV, which seriously affects precision of object tracking in practical application. The traditional “centroid method” or “template matching method” have been difficult to meet the requirements of high precision miss distance which was less than 1pixel(RMS) under the reflected light interfering. In order to developing operational effectiveness of weapon system, G-DS(Gray weighted factor-Diamond Search method) algorithm was proposed which combined with gray weighted factor based on self-learning mechanism. It has been studied for the characteristics of UAV images by field experiment. The results show that G-DS algorithm is low-latency(less than 5ms), which can reduce time complexity compared with the traditional ME algorithm, furthermore, G-DS algorithm was robust based on local motion vector of the block, which can improve ability of target detection and recognition compared with the traditional “centroid method” or “template matching method”. Hence, G-DS algorithm was beneficial to the engineering of high-energy laser weapon.

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