{"ID":2843342,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08140","arxiv_id":"2511.08140","title":"PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions","abstract":"Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (\u003c= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and high-resolution (1280 x 720) Event-RGB dataset for object detection under challenge conditions. PEOD contains 130+ spatiotemporal-aligned sequences and 340k manual bounding boxes, with 57% of data captured under low-light, overexposure, and high-speed motion. Furthermore, we benchmark 14 methods across three input configurations (Event-based, RGB-based, and Event-RGB fusion) on PEOD. On the full test set and normal subset, fusion-based models achieve the excellent performance. However, in illumination challenge subset, the top event-based model outperforms all fusion models, while fusion models still outperform their RGB-based counterparts, indicating limits of existing fusion methods when the frame modality is severely degraded. PEOD establishes a realistic, high-quality benchmark for multimodal perception and facilitates future research.","short_abstract":"Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (\u003c= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address thes...","url_abs":"https://arxiv.org/abs/2511.08140","url_pdf":"https://arxiv.org/pdf/2511.08140v1","authors":"[\"Luoping Cui\",\"Hanqing Liu\",\"Mingjie Liu\",\"Endian Lin\",\"Donghong Jiang\",\"Yuhao Wang\",\"Chuang Zhu\"]","published":"2025-11-11T11:50:31Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}