---
title: "计算机视觉开发服务"
canonical: https://wavect.io/zh/services/computer-vision/
language: zh
description: "面向图像分类、目标检测和视觉质检的计算机视觉开发。Wavect 评估真实采集条件，并将可复核的结果接入业务流程。"
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

计算机视觉

# 把图像变成有用的信号，同时处理好困难场景。

在精选图片上表现良好的模型只是开始。我们围绕实际相机、光照、运动和对象构建视觉工作流程，并验证系统应如何处理不确定或陌生的输入。

合作范围

## 采集条件本身就是需求的一部分。

1. **理解现场** 明确对象、相机位置、变化条件和错误成本。
2. **衡量识别效果** 通过带标注的样本和困难条件比较不同方案。
3. **接入实际流程** 连接预测、不确定性和人工复核。

直接说明

计算机视觉开发将图像或视频输入连接到约定的识别、检测或检查任务。范围包括采集约束、带标注的评估数据、模型选择，以及与人工复核或业务操作的集成。

适合以下需求

- 拥有重复性视觉检查或识别任务的运营流程
- 需要在已知采集条件下使用图像信号的软件产品

其他需求路径

- 主要目标是提取并核验文字字段的文档流程
- 未经领域验证的安全关键自主决策

## 明确相机需要区分什么

分类为一张图像赋予类别；检测定位对象；分割识别图像中的区域。我们从业务行动选择任务。双方定义重要类别、什么算缺陷或例外，以及输出是帮助人员判断，还是触发范围明确的自动操作。任务定义决定标注与评估方式。

## 优化模型之前先检查图像来源

我们检查分辨率、光照、视角、遮挡、运动，以及不同地点或设备之间的差异。调整采集方式可能比追加训练更有价值。评估集必须包含真实困难样本和未知输入。如果尚无标签， [数据标注](/zh/services/data-annotation/) 会先定义规则和质量复核流程。

## 有意识地选择推理位置

我们按照识别质量、延迟、硬件和运行成本比较现成模型、适配模型及定制训练。设备端推理可以减少网络依赖，服务器则可能便于更新。方案会说明哪些数据离开设备、图像如何保留，以及如何记录模型版本。设备连接可与 [工业物联网](/zh/services/industrial-iot/) 分别确定责任。

## 示例验收：关键部件被部分遮挡

一个示例检查收到关键部件被遮挡的图片。流程将结果记为无法确定，并要求重新拍摄或人工复核，而不会自动认定该部件合格。测试分别覆盖可见缺陷、合格对象和无法使用的图像。验收记录将判断与采集条件、模型版本对应起来。

## 同时交付评估和系统集成

你将获得类别定义、允许使用的数据集引用、训练或配置文件、评估结果和应用集成。双方约定阈值，并在相机或环境出现重要变化后重新测试。操作人员可以看到已知限制。初始范围验收后，可以通过 [MLOps](/zh/services/mlops/) 增加持续发布与监控机制。

## 开始前的常见问题

### 可以使用现成的视觉模型吗？

可以。我们首先检查合适的模型或 API 是否满足任务和数据要求。只有测量发现不足时，才考虑进一步定制训练。

### 你们开发相机硬件或固件吗？

本服务涵盖软件和约定集成。相机选型、安装、固件修改及现场作业分别列明，以便清楚划分责任。

### 系统能检查所有产品变体吗？

支持范围根据有代表性的证据确定。新的材料、形状或采集方式可能需要重新评估，才能加入已接受的运行范围。

### 如何报告质量？

我们采用与任务匹配的指标，并按相关类别或条件报告错误。漏检、误报和不可用图像分别呈现，因为它们对运营造成的成本不同。

## 选择合适的下一步

- [创建并复核视觉标签](/zh/services/data-annotation/)
- [管理视觉模型的版本发布](/zh/services/mlops/)
- [从文档中提取业务记录](/zh/services/document-ai/)

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