SCTF-Det: Siamese Center-Based Detector with Transformer and Feature Fusion for Object-Level Change Detection

• Published in: Chinese Automation Congress (Online) pp. 8788 - 8793
• Main Authors: Huo, Jiaxin, Sun, Lihang, Liu, Jianyi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.11.2023
• Subjects: Change Detection; Detectors; Feature extraction; Head; Object-Level; Real-time systems; Redundancy; Semantics; Siamese Network; Transformers; Vision Transformer
• ISSN: 2688-0938
• DOI: 10.1109/CAC59555.2023.10451045

Current Scene Change Detection(SCD) methods are widely used in various subject areas, with detection granularity mostly limited to pixel-level. However, for certain practical applications such as garbage detection and traffic monitoring, the overall changes of object-level instances are more concerned so that fine-grained results may not be necessary, incurring excessive computational redundancy and insufficient real-time performance. To address the issue, we propose a one-stage object-level change detection framework named Siamese Center-Based Detector with Transformer and Feature Fusion (SCTF-Det), aiming at using less computing resources while still obtaining object-level change information, such as appearance or disappearance of objects. We adopt Siamese Vision Transformer to efficiently capture global semantic features and design differential feature fusion and multi-scale fusion to better fuse the features coming from image pairs. Instead of using a segmentation head like most SCD methods, we use a detection head to capture changed objects or regions. Moreover, we introduce a gating mechanism in image pairs and automatically mark the bounding box on the corresponding "Appear" change region. The experiments are conducted on VL-CMU-CD and CDNet2014 datasets, with Fl scores of 78.6% and 83.6% respectively. Our SCTF-Det substantially improves inference speed by 3-5 times compared to the existing methods.