发表机构
Nanchang Hangkong University; Jiangxi Expressway Petrochemical Co., Ltd.（南昌航空大学; 江西高速公路石化有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对石化物流节点危险品转运合规问题，提出GSO-Net基准数据集，含超5万帧和SOP层级状态，用于视觉SOP理解，揭示现有模型在细粒度状态感知上的不足。
AI 中文摘要
石化物流节点的危险品作业对智能交通系统而言是安全关键的，然而现有的视觉基准很少在现实部署约束下解决程序合规问题。在大型基础设施网络中，摄像头通常以稀疏轮询方式运行，因此转运状态必须从不完整的观测和局部证据中推断。我们提出了GSO-Net，一个用于石化卸货场景中标准操作程序（SOP）视觉理解的大规模基准。据我们所知，GSO-Net是首个专门用于石化危险品转运场景中视觉SOP理解的公开基准数据集。它包含来自64个真实高速公路石化物流节点的超过50,000个独立采样帧，并采用从SOP派生的层级结构，将9个宏观程序步骤与15个微观操作状态相关联。定义了两种任务：作为核心基准的微观状态和宏观步骤的联合检测，以及作为诊断参考的帧级步骤分类。使用轻量级、基于Transformer、开放词汇和整体模型的实验揭示了对象感知与转运阶段理解之间的明显差距。当前模型在接触级状态接地、瞬态步骤识别和阶段一致性方面仍然薄弱，尤其是在稀疏轮询、微小关键目标和长尾操作证据的情况下。GSO-Net为危险品运输中的细粒度状态感知和基于视觉的安全监控提供了一个实用基准。该数据集可在https URL公开获取。
英文摘要
Hazardous-freight operations at petrochemical logistics nodes are safety-critical for intelligent transportation systems, yet existing vision benchmarks rarely address procedural compliance under realistic deployment constraints. In large infrastructure networks, cameras often operate under sparse round-robin polling, so transfer status must be inferred from incomplete observations and localized evidence. We present GSO-Net, a large-scale benchmark for visual understanding of standard operating procedures (SOPs) in petrochemical unloading scenarios. To our knowledge, GSO-Net is the first public benchmark dataset dedicated to visual SOP understanding in petrochemical hazardous-freight transfer scenarios. It contains over 50,000 independently sampled frames from 64 real expressway petrochemical logistics nodes and adopts an SOP-derived hierarchy linking 9 macroscopic procedural steps with 15 microscopic operational states. Two tasks are defined: joint detection of microscopic states and macroscopic steps as the core benchmark, and frame-level step classification as a diagnostic reference. Experiments with lightweight, transformer-based, open-vocabulary, and holistic models reveal a clear gap between object perception and transfer-stage understanding. Current models remain weak on contact-level state grounding, transient step recognition, and stage consistency, especially under sparse polling, tiny critical targets, and long-tailed operational evidence. GSO-Net provides a practical benchmark for fine-grained state perception and vision-based safety monitoring in hazardous freight transportation. The dataset is publicly available at https://github.com/yuxieHarrison/GSO-Net
