在实验室训练视觉模型，实现仓库叉车异常检测跨环境通用。
Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems
- 在实验室模拟环境完成摄像头布置与图像触发策略设计
- 结合模型选择与集成，使模型在真实仓库中保持高检测准确率
- 适合希望减少标注和重训成本的仓储自动化团队
在传统仓库部署计算机视觉模型需大量资源进行摄像头安装、图像采集、标注、训练与部署，且常因摄像头位置限制和环境差异需重复操作。本文探索一种创新方法：仅在实验室环境下完成标准流程，聚焦垂直物料搬运系统中的叉齿异常检测。通过大量实验发现，优化摄像头布局、合理图像触发机制、精心挑选并集成模型，可实现从实验室到多样真实仓库环境的有效泛化。该方法有望将仓库自动化部署简化为仅需摄像头安装、图像采集与模型部署，大幅节省图像标注与模型重训所需的时间与资源。本研究为实验性探索，非生产级部署。
原文摘要 · Abstract (English)
Deploying computer vision models in Warehouse Facilities traditionally requires extensive resources for camera mounting, image collection, annotation, training, and deployment - a process often needing repetition in each new environment due to camera mounting constraints and environmental variability. This paper explores an innovative approach to streamline this process by conducting the standard procedure solely in a laboratory setting, focusing on vertical material handling systems and anomaly detection in forks of the systems. Through extensive experimentation, we have found that combining optimal camera placement, strategic image triggering, careful model selection and model ensemble enables effective generalization from laboratory conditions to diverse warehouse facilities environments, potentially transforming warehouse automation implementation by simplifying warehouse facilities deployment to just camera mounting, image collection, and model deployment, thereby saving significant resources and time typically spent on image annotation and model retraining. This is an experimental research study and not a production deployment.
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