港口带式输送设备运行安全数据集

本数据集聚焦港口带式输送设备安全监测场景，针对大块异物识别典型安全风险构建高质量标注样本，可直接用于 AI 模型训练。数据集采用工业标准VOC-XML格式标注，包含设备运行现场高清图像信息、图像尺寸、存储路径等完整元信息，标注规范、结构清晰、兼容性强。数据覆盖真实作业环境，样本质量经过校验，无脏数据与冗余信息，能有效支撑异物检测、安全预警等算法开发。

This dataset focuses on the safety monitoring scenario of belt conveying equipment in ports. It constructs high-quality annotated samples targeting typical safety risks in large foreign matter identification, and can be directly applied to AI model training. The dataset adopts the VOC-XML industrial standard annotation format, and contains complete metadata including high-definition image information of the equipment operation site, image dimensions, storage paths and other related content. It features standardized annotation, clear structure and strong compatibility. The dataset covers real working environments, and the quality of all samples has been verified, with no dirty data or redundant information, which can effectively support the development of algorithms such as foreign matter detection and safety early warning.

搜集汇总

背景与挑战

背景概述

该数据集面向港口带式输送设备安全监测场景，专门针对大块异物识别风险构建，包含经质量校验的高清现场图像及采用VOC-XML格式的标准标注信息。数据集结构规范、质量可靠，可直接用于训练目标检测等AI模型，以支撑异物检测与安全预警等算法开发。