ESOD: Event-Based Small Object Detection

摘要

Event-based object detection plays a crucial role in scenarios involving high-speed motion, extreme lighting conditions, and high-frequency detection. However, existing methods fail to address the challenges posed by small objects, including discriminative feature deficiency, the loss of critical information, and the inherent sparsity of event data. Moreover, the lack of benchmark datasets has significantly hindered progress in this field. To tackle these issues, we propose the Fully Deformable Detection Network (FDDNet), a lightweight framework that dynamically adapts to extract key features. First, we introduce a Long-Term Deformable Temporal Receptive Module (LDTR), which aligns critical features across consecutive event streams and leverages a State Space Model for long-range temporal modeling, enhancing the detection of high-speed small objects. Second, to address the sparsity of event data and the concentration of key features along object edges, we design a Sparse Feature Aggregation Block (SFAB) within the backbone and a coarse-to-fine deformable detection head, enabling hierarchical feature refinement from local to global, and improving the detection quality of sparse targets. Finally, to mitigate the lack of event-based small object datasets, we develop a high-quality, annotation-free data acquisition method and collect a real-world benchmark dataset for validation. Extensive experiments demonstrate that our approach achieves state-of-the-art (SOTA) performance on event-based small object detection tasks, with a mAP of 37.4% (+2.4%) on our benchmark and runs at 88 FPS, showcasing both accuracy and real-time capability. Our code and Supplement are available at https://github.com/Lqm26/ESOD.