基于计算机视觉的番茄损伤自动检测与分类研究

Automatic identification and classification of tomatoes with bruise using computer vision

为了提高番茄损伤检测与分类的准确率和效率,综合运用计算机视觉技术、BP算法、人工神经网络技术,实现番茄损伤的自动检测与分类。首先,通过计算机视觉系统获取番茄图像,利用图像处理去除噪声、图像分割、图像增强等多种基本图像处理的方法对番茄损伤图像进行了处理,综合运用并行和串行区域分割技术进行番茄表面缺陷区域检测。其次,对番茄图像进行了特征分析,通过提取三种特征包括8个特征参数,采用改进的BP算法训练的多层前向人工神经网络对番茄的损伤进行分类。该文中缺陷检测方法和特征提取方法的采用,使该计算机视觉系统节省了时间,提高了精度。试验证明番茄损伤检测和分类的准确率不低于90%。

To improve the accuracy of detection and classification of tomatoes with bruise, computer vision, BP algorithm and artificial neural network technology were synthetically applied to automatically identify and classify the tomatoes with bruise. First, the images of tomatoes were captured through computer vision system, then the images of tomatoes with bruise were processed applying three methods that include filtering noise and dividing images and highlighting images to identify bruise images of tomatoes applying distriction increasing. Second,multilayer forward artificial neural network trained with BP algorithms was employed to classify tomatoes, with bruise. The computer vision system using the presented defect detection method and image extraction technology can save time and raise precision.The experiments show that the rate of testing precision was not less than 90%.