基于机器视觉的汽车换挡面板自动分类方法

针对报废汽车企业对不同型号汽车换挡面板自动分类的需求,提出了一种基于机器视觉的面板自动分类方法。该方法首先使用Blob分析对图像进行预处理,分割出图像中的换挡面板目标对象,接着提取被分割图像的11个形状特征作为特征向量,利用特征向量训练多层感知器(Multi-Layer Perceptron,MLP),最后使用完成训练的MLP神经网络模型对不同类型的汽车换挡面板进行分类预测。实验结果表明,该方法的分类准确率达到99%,每幅图像的分类时间小于100 ms,验证了该方法的有效性与高效性,能够作为报废汽车换挡面板自动分类的一种手段。

Automated classification method for car gear shifting panel based on machine vision

For the needs from scrapped car companies to automatically classify the car gear shifting panels of different models,an automated classification method of car gear shifting panel based on machine vision was proposed.The Blob analysis was used for preprocessing,when image was segmented to locate the target of gear shifting panel.Then shape feature extraction was carried out to find 11 shape feature as characteristic vector,which was the input of trained MLP model.Finally,by using complete trained MLP model,different types of car gear shifting panel were classified.Experimental results showed that the classification accuracy of this method is 99%,and the classification time of each image is less than 100 ms,which verifies the feasibility and efficiency of this method,and can be used as an effective approach of automatic classification of shifting panel of scrapped cars.