基于机器视觉的机场托运行李系统行李异常滞留检测报警方法OA

During the operation of the airport baggage handling system,the tray sorting machine needs to handle a large number of luggage,which may lead to delays in luggage loading due to sorting abnormalities.To address this issue,a method is proposed that utilizes real-time image processing of surveillance videos to detect abnormal luggage that is causing delays.First,the video stream is periodically sampled to obtain template images under normal operating conditions and the images to be processed.Image HOG feature vectors are extracted,and the Cosine Similarity is calculated to obtain a preliminary match result.Then,a perceptual hash algorithm is applied to refine the match results based on the initial comparison.The complementary nature of these two algorithms provides the final detection results.Through a 12-hour test involving 24 cases of abnormal luggage,it is demonstrated that,compared to the YOLOv5 model on the coco dataset and an improved frame difference method,the detection rate is improved by 20.8%and 37.5%respectively.Moreover,the false positive rate is reduced by 8.3%and 20.8%,respectively.This method is successfully tested and can be deployed at the tray sorting machine slots to enhance the efficiency of the baggage handling system.