Research on intelligent analysis and identification of visualization scenes in transport supervision hall based on image processing technology

This paper firstly investigates the visual scene testing method with image processing technique and predicts the number of scenes by UML structure. Secondly, the scene recognition of the transport supervision hall is performed by using image processing technology, and the ant colony optimization algorithm is proposed for local search to update the scene information and edge extraction. Then, the ED-AlexNet network model is constructed to detect and identify the target scenes. Finally, an error matrix is introduced to calculate the confidence of the sample model distribution in the test set, and the recognition extraction performance and recognition accuracy of the ED-AlexNet network model are analyzed. The study shows that when the error matrix is introduced, the highest value of ED-AlexNet F – measure is close to 0.9, and the end value is over 160, which has a good performance of scene target recognition extraction. The average recognition accuracy of ED-AlexNet is higher than 95%, with good compatibility and high accuracy of recognition.