Baggage Specification Detection Based On The Binocular Vision

To implement fully "simplify business" program putted forward by IATA(international air transport association) and shorten the boarding time, researching the self-service luggage system has become an important solution. Passengers can use it to make the identification and print boarding pass and baggage tag. The system can automatically detect baggage information, and finally complete luggage checked. Among them, the baggage information detection includes baggage weight collection, specifications detection and luggage label scanning. For the luggage size automatic detection problem, a set of detection algorithm based on binocular stereo vision is proposed to realize non-contact detection on the self-service luggage system.First of all, according to the hardware environment of self-service luggage system and binocular stereo vision structure model, It designs luggage image acquisition hardware system of the parallel binocular structure. At the same time, the camera imaging model and calibration method were studied, using a system calibration method based on the chessboard plane to get the camera parameters.Secondly, using the optimal threshold value method to preprocess the images. Then, in feature extraction, designing 2D canny edge detection algorithm to describe the luggage geometric shape. After that, According to basic constraints and disparity gradient additional constraint conditions, designing the regional gray matching method based on feature, and using the two-way matching strategy, reducing the error matching, It ensures the reliability and stability of the matching.Finally, researching the calculation methods of space point 3D information of the binocular vision system. According to the parallel binocular structure, using the triangular method to calculate 3d information of the baggage.Based on the technology and method mentioned above, testing the shape of different size luggage can realize the non-contact detection of baggage specifications.