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 (≤ 640 × 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and hign-resolution (1280 × 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 will be publicly released later to facilitate future research.

The advent of Software Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the deployment of software-based routing and forwarding devices in cloud and network systems. Yet, the performance of IPv6 route lookup in these devices remains a significant challenge due to two main factors: 1) the longer IPv6 addresses, which hinder high-speed lookup, and 2) the huge IPv6 IP address space, necessitates adaptability to varied length-based prefix distributions across various network scenarios. Existing IP lookup algorithms fall short in addressing these IPv6-specific challenges. To address these challenges, this paper proposes a novel Heuristic Binary Search (HBS) scheme. Building on the classical “Binary Search on Prefix Lengths” scheme, HBS employs three innovative techniques to achieve adaptive and fast IPv6 lookups with incremental updates: 1) a heuristic binary search method for fast lookup; 2) a tree rotation method for dynamic adjustment of binary search tree shapes in response to changes in prefix distribution; and 3) the introduction of the Associated Marker List (AML), a new data structure aimed at facilitating rapid incremental prefix updates. Our theoretical proofs and comprehensive evaluations demonstrate HBS’s superiority in lookup performance, dynamic adaptability, update speed, and memory efficiency.

The advent of Software Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the deployment of software-based routing and forwarding devices in modern network architectures. However, IPv6 route lookup remains a substantial performance bottleneck in these software-based devices due to two key challenges: (1) the longer addresses and prefixes, which hinder high-speed IPv6 lookup, and (2) the larger address space of IPv6 necessitates adaptability to varied length-based prefix distributions across various network scenarios. Current trie-based methods like SAIL and Poptrie have enhanced IPv4 lookup, but they struggle with adaptive and fast IPv6 lookup due to their fixed search scheme from short to long prefixes. To overcome these challenges, we propose a novel Heuristic Binary Search (HBS) scheme to achieve adaptive and fast IPv6 lookup. HBS refines the traditional "Binary Search on Prefix Lengths" scheme by incorporating two key techniques: (1) a heuristic binary search method for accelerated lookup and (2) a tree rotation method for dynamic adjustment of binary search tree shapes in response to changes in prefix distribution. Our evaluation of HBS demonstrates its superiority in terms of lookup throughput, update speed, memory efficiency, and dynamic adaptability.