AI机器视觉导线颜色缺陷质检数据

通过对产品导线颜色识别数据进行收集，采用摄像头拍摄照片，按照一定规则存储在硬盘，采集不同产品种类、不同线径、不同室内光照条件、不同镜头倍率的数据信息并进行分类存储。随后进行人工数据标注，对合格与不合格情景的进行标注，人工标注分割各种场景的数据。随后开展训练模型，训练带有图像分割功能的目标检测深度学习模型算法，提高其对于该类场景数据的分割检测准确性.在生产制造环节，将模型部署到多媒体记录系统、机器视觉系统中，帮助操作人员辨识清楚导线的管脚位置，多色导线的排列模式，数量等数据，与设计文件、工艺标准进行比对，判断产品颜色排列模式是否正确。

Data collection for product wire color recognition is conducted: photos are captured by cameras, stored on hard disks in accordance with specified rules, and data covering different product types, wire diameters, indoor lighting conditions and lens magnifications are collected, categorized and stored. Subsequently, manual data annotation is performed: scenarios of qualified and unqualified products are annotated, and data from various scenarios are segmented via manual annotation. Thereafter, model training is carried out: a deep learning-based object detection algorithm with image segmentation function is trained to improve the segmentation and detection accuracy of this type of scenario data. During the production and manufacturing stage, the model is deployed to multimedia recording systems and machine vision systems to help operators identify the pin positions of wires, the arrangement patterns and quantities of multi-color wires and other related data, and compare these with design documents and process standards to determine whether the color arrangement pattern of the product is correct.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于制造业产线导线颜色缺陷质检的机器视觉数据集，规模为14.8MB，月更新。包含不同条件下的导线图像数据，经过人工标注，用于训练深度学习模型以提高颜色分割和检测的准确性。