Feature-Enhanced Convolutional Attention for Unstable Rock Detection in Aerial Images

This study introduces the feature-enhanced convolutional attention integration (FEC-AI) module, an innovative convolutional neural network (CNN) algorithm specifically tailored for the meticulous detection of small, challenging objects in high-resolution remote-sensing imagery, emphasizing unstable rock formation monitoring. FEC-AI significantly advances CNN-based feature extraction and representation through a combination of advanced techniques. These include the cross-layer attention module (CLAM), which enriches feature maps with multiscale contextual details; the offset-aware adjustment module (OAM) for precise spatial refinement of feature localization; and the contextual feature aggregation (CFA) process, which synergizes these refined features for enhanced detection efficacy. Concurrently, we introduce the RSUR-2D dataset, a comprehensive compilation of 1557 rigorously annotated images depicting karst landscapes around Beijing, expressly designed to challenge and advance remote-sensing algorithms in geological hazard analysis. Through extensive testing on the RSUR-2D and established COCO datasets, the FEC-AI module demonstrated outstanding performance in small object detection, achieving a mean average precision at mAP₅₀ of 66.0% and mAP_s of 21.1% on the RSUR-2D dataset. The RSUR-2D dataset, a valuable resource for the research community, is publicly accessible at https://github.com/chenmu1204/czx.