基于图像识别轮胎变形的非接触式车辆称重方法

为了解决接触式车辆称重方法存在的安装和维修成本高、使用年限短、识别精度低等问题,创新性地提出一种基于计算机视觉获取轮胎变形的非接触式车重识别方法。首先,利用视频图像采集装置拍摄车辆轮胎图像信息,通过图像处理技术提取轮胎轮廓,并根据轮廓变形计算轮胎的垂向挠度。其次,通过胎压监测系统(TPMS)获取轮胎的真实胎压值,对于没有安装TPMS的车辆,则可以通过图像字符识别技术读取轮胎侧壁的胎压标识信息,再利用统计回归方式确定实际胎压值。在此基础上,将轮胎垂向挠度和胎压值代入推导的称重公式计算轮胎承受的重量,再将所有轮胎承受重量求和得到车辆总重量。最后,以现场的乘用车和重载货车为例,验证在不同胎压和重量变化下非接触式车辆称重方法的准确性,并对比分析3个称重公式的准确性。研究结果表明:车重识别准确率随着胎压增大而降低,随着车重增大而上升;轮胎刚度拟合公式的载重识别准确率达到95%以上,高于理论推导公式和半经验拟合公式。提出的非接触式车辆称重方法具有测量范围广、无需任何额外传感设备、不用封闭交通和易于信息集成等优势,有效地突破了现有接触式车重识别技术的瓶颈,具有很好的工程应用前景。

Prediction of shelled shrimp weight by machine vision

The weight of shelled shrimp is an important parameter for grading process.The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness.In this paper,a multivariate prediction model containing area,perimeter,length,and width was established.A new calibration algorithm for extracting length of shelled shrimp was proposed,which contains binary image thinning,branch recognition and elimination,and length reconstruction,while its width was calculated during the process of length extracting.The model was further validated with another set of images from 30 shelled shrimps.For a comparison purpose,artificial neural network(ANN) was used for the shrimp weight predication.The ANN model resulted in a better prediction accuracy(with the average relative error at 2.67%),but took a tenfold increase in calculation time compared with the weight-area-perimeter(WAP) model(with the average relative error at 3.02%).We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.