摘 要
触点零件是工业中重要的电器元件，完成关键的切换功能，是决定电器使用寿命的主要因素，触点零件的表面形貌对零件的接触性能起着相当重要的作用。目前国内检测手段落后，采用计算机视觉检测的方法代替人工具有很大的实际应用价值。针对触点零件形貌多样的特点，本文研究开发能够实现自适应学习功能的在线视觉检测系统。
论文通过对检测需求的分析，设计了系统的整体结构和自学习检测流程，详细讨论了系统各个组成部分的功能及选型，并设计了软件系统的功能模块及工作流程。
根据检测系统实时性和准确性的要求，本文在图像处理阶段研究了基于高斯滤波模板的快速有效图像预处理方法，针对触点零件图像特点提出了一种基于改进极小值点的自适应阈值分割算法和多图中值阈值确定算法，并构建了５大类图像特征库，共包含４５个特征。 通过对多类别模式识别主要因素的分析研究，提出了特征预处理方法和基于典型变量的特征优化算法，设计了一种两类浮动搜索－多类后向选择算法实现多类别特征选择，并结合支持向量机多级二叉树的多类别分类策略，共同实现了多类别触点零件快速学习和分类检测。
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跳跳鞋
在上述工作基础上，论文搭建了试验硬件平台，开发了实现上述算法的相关软件，对多组零件进行了试验，取得了较好的试验结果。论文也分析了误差来源，提出进一步研究改进的方向。
关键词：触点形貌，计算机视觉，在线检测，图像处理，多类模式识别
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RESEARCH ON ONLINE SELF-LEARNING
VISION DETECTION SYSTEM
OF CONTACT APPEARANCE
ABSTRACT
As an important industrial electrical component, contact component decides electric equipments’ life. The surface appearance features of contact component play an important
role in contact performance. The detection methods depending on manual stay low level,
脉冲信号using computer vision technique to replace human vision in quality detection has great
practical value. Aimed to different varieties of contact appearance features, the online
vision detection system achieving self-learning function is researched in this thesis.
Firstly, the structure and self-learning process of detection system are designed
through the detection requirements analysis. The function parts and their selection
methods are discussed in detail. The function modules and workflow of software system打塞机>排火
are designed.
In image processing, according to real-time and accuracy requirements, the Gaussian
filter template is studied as a quick and efficient image pre-processing method. An
adaptive threshold segmentation method based on improved minimum point algorithm and
multi-graph median algorithm is proposed. And the 5 major categories of image feature
library is built, which includes 45 features.
Through the analysis of major factors that influence the multi-category pattern recognition, the feature pre-processing method and optimization method based on
canonical variables are put forward. To realize multi-category feature selection, a
two-category floating search and multi-category backward selection algorithm is designed.
Combined these algorithms with SVM multi-level binary tree classification strategy, the
system realizes the multi-category quick self-learning and classification detection.
汽车安全带卡扣Based on the work mentioned above, the experimental platform is developed. The experimental system is tested through several groups of contact components and gets good
results. The future research direction is proposed after the error analysis.
KEY WORDS: contact component appearance, computer vision, online detection, image processing, multi-category pattern recognition
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