CTPEM: A Cross-Temporal Progressive Enhancement Model Tackling Object-Level Building Damage Detection and Vanishing Small Features

The detection and statistics of damaged buildings are crucial for rescue work, especially when detecting object-level and small buildings. Existing detection technologies mainly focus on pixel-level analysis, often ignoring the significance of object-level analysis and the identification of small buildings. To address these challenges, a Cross-Temporal Progressive Enhancement Model (CTPEM) is proposed. In CTPEM, the Visual Center Guided Enhancement (VCGE) module is proposed, which uses multiple visual centers to learn the binary relationship between pre- and post-temporal features to capture global and local information. Meanwhile, the Progressive Small Object Enhancement (PSOE) module is proposed, which is used to capture tiny and small-sized features through multiple feature maps at varying depths, aiming to reduce the influence of gradually vanishing features. Compared with state-of-the-art (SOTA) methods, CTPEM achieves superior performance in detecting damaged buildings, particularly small and tiny targets. Extensive experimental results demonstrate the effectiveness and practical value of our approach for accurate casualty assessment, and further facilitate more efficient disaster response and optimal resource allocation. The CTPEM code is available at https://github.com/GZPLHJ181107/CTPEM.