SAED:Structure-Aware Event Dataset

SARE (Structure-Aware Reliability Evaluation Dataset), <b>accepted for publication at the </b><b>International Joint Conference on Neural Networks 2026</b>, is a structure-centric event-based dataset designed for analyzing spatio–temporal structural reliability in neuromorphic vision systems.<br>Unlike conventional task-driven benchmarks, SARE focuses on controlled structural variation rather than semantic diversity, enabling systematic study of how structural degradation affects neural inference. The dataset is built using geometry-driven objects with clear edges and contours, allowing interpretable evaluation of structural integrity.It contains approximately 27,000 event clips captured using a DVXplorer event camera (640×480 resolution) under controlled indoor conditions. The dataset is organized across object geometry, spatial scale (0.5×, 1.0×, 1.5×), and motion conditions (object motion and camera motion), providing diverse yet comparable spatio–temporal event structures.A key feature of SARE is the inclusion of standardized structural degradation protocols, including noise injection, over-denoising, and localized structural removal. These perturbations are designed to decouple structural corruption from global event statistics, enabling reliable diagnosis of silent failure in both spiking and non-spiking neural models.SARE is intended for reliability analysis, robustness evaluation, structure-aware learning, and the development of diagnostic metrics for event-based vision.