Research And Implementation Of Forbidden Target Detection Algorithms In Elevator Car Based On Surveillance Video

Detection of forbidden target based on surveillance video has strong academic and engineering meaning for elevator security.According to the requirement of Jiangsu Province Special Equipment Safety Supervision Inspection Institute.Branch of Wuxi,the detection algorithms of forbidden target based on elevator surveillance video are studied and the subsystem is designed.The object detection will not work if there is nothing in the elevator.Dog is selected as prohibited target to carry out experiments in this paper,and three different types of object detection algorithms are implemented.Algorithm based on HOG and SVM is applyled and the recognition effect based on different features is compared.The algorithm using HOG combined with SAE improves the recognition rate of the original algorithm by about 10%.YOLO-based algorithm is improved by adding BN operation and a better loss function.After improvement,the recall rate is up to 83%.Finally,the processing results of the three algorithms are compared.Experiments show that the improved YOLO algorithm meets the requirements of online object detection.In this paper,a forbidden target detection subsystem based on elevator video is designed and implemented with B/S architecture.Target detection,target detection data storage and other functions are completed and a web to display the detection results is built.The detection system can identify the prohibited target,and the system runs steadily in the experimental car.