Hebei University of Technology
When the current security robot is used to detect dangerous goods under the vehicle, it is mainly based on visual recognition, limited by many conditions such as light, background, and exposure time. The recognition accuracy is low and the efficiency is low. In view of the above problems, the information of gas sensor and radiation sensor is used as supplementary input, and the information of foreign matter rate, radiation intensity and gas concentration in the image is integrated. A fusion algorithm based on cloud model and combining DS evidence theory and weighted average is proposed and designed. Dangerous goods identification system software under the vehicle. Through the test experiment of the vehicle dangerous goods identification device, it is proved that the algorithm of this paper can complete the goal of vehicle dangerous goods identification, and can classify the dangerous goods accurately, which has good practical application value.
高春艳. 基于多传感器信息融合的车底危险品分类识别算法[J]. 科学技术与工程, , ():复制