| dc.description.abstract | In recent years, due to the vigorous development of deep learning networks, deep learning has been widely used in the field of computer vision and graphic recognition. Because of the development of artificial intelligence, through the establishment of intelligent systems, it can effectively help humans to handle simple and repetitive problem.
We propose an automated incorrect scene detection system. which can effectively replace the process of manually detecting the correctness of the landmark image database to save human resources costs.
In the system of automatic incorrect scene detection, we propose an incorrect scene detection algorithm to solve the problem of detecting incorrect scenes, and based on this system architecture, we propose a MLE (Multiple Level Extractor). By extracting different levels of features in the scene image, improved the feature extraction effect of the Resnet50 network architecture. In addition, we also propose MSD(Multiple Scale Distance) measurement, which sums the feature distances at various scales under a given feature extractor. MSD also improved the performance of the system. Finally, based on the architecture of the system, we experimented with the system under different feature extractor and distance measurement methods , which affect the degree of system performance change. | en_US |