验证边界框标注

April 23, 2026

Ritabrata Chakraborty

Software Engineering Intern

软件工程实习生

Harshith Batchu

Machine Learning Engineer II

机器学习工程师 II

Ishan Nigam

Senior Machine Learning Engineer

高级机器学习工程师

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Introduction

引言

Despite advancements in open-source models, enterprises often require use-case-specific models to solve their unique business challenges. Training these models necessitates high-quality data, which is frequently collected via human annotations. While effective, these annotations often suffer from human errors, which affect model quality.

尽管开源模型取得了进步，企业通常需要特定用例的模型来解决其独特的业务挑战。训练这些模型需要高质量数据，这些数据通常通过人工标注收集。尽管有效，这些标注往往存在人为错误，从而影响模型质量。

Uber AI Solutions provides industry-leading data labeling solutions for enterprise customers and plays a crucial role in enabling organizations to annotate data efficiently. To guarantee high-quality data for our clients, we’ve developed several internal technologies. This blog focuses on one such technology: our ML-based bounding box validation system. This process specifically detects and helps resolve labeling errors in bounding boxes in videos, ensuring issues are fixed before submission.

Uber AI Solutions 为企业客户提供行业领先的 data labeling 解决方案，并在使组织高效 annotate data 方面发挥关键作用。为了向客户保证高质量数据，我们开发了几种内部技术。本博客重点介绍其中一种技术：我们的 ML-based bounding box validation system。该过程专门检测并帮助解决视频中 bounding boxes 的 labeling errors，确保问题在提交前得到修复。

Background

背景

Bounding box annotation in videos is essential for building object trackers, which are core components of ML and robotics systems. These videos are usually very long. To mitigate fatigue during the annotation process, they are typically split into smaller segments, with different operators working on each. The segments are then joined post-annotation. However, this split and merge is a potential source of errors.

视频中的边界框标注对于构建对象跟踪器至关重要，而对象跟踪器是 ML 和机器人系统的核心组件。这些视频通常非常长。为了减轻标注过程中的疲劳，通常将它们分割成较小的片段，由不同的操作员处理每个片段。然后在标注后将片段合并。然而，这种分割和合并是潜在的错误来源。

The reliance on human annotators inherently leads to mistakes. W...