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Object Helps U-Net Based Change Detectors
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DOI：10.1109/JAS.2023.124032.png)
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This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change detector. Change detection is fundamental to many computer vision tasks. Although existing solutions based on deep neural networks are able to achieve impressive results. However, these methods ignore the extraction and utilization of the inherent object information within the image. To this end, we propose a simple but effective method that employs an excellent object detector to extract object information such as locations and categories. This information is combined with the original image and then fed into the U-Net based change detection network. The successful application of our method on MU-Net and the experimental results on CDnet2014 dataset show the effectiveness of the proposed method, and the correct object information is helpful in change detection.
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