强力输送带接头点识别算法

针对新型的强力输送带以接头点为基准参考点进行故障实时检测，存在接头定位困难问题，提出了一种基于统计学和图像处理技术相结合的接头点检测识别算法。该算法首先把图像每一列看成一个样本总体；然后根据每一列的样本均值以及该列中的每一个像素的方差计算出该像素点的平滑度，根据一定的平滑度阈值来找到接头点区域，通过对接头点区域应用图像处理技术中的腐蚀、膨胀等运算，使其形成一个矩形区域。利用该矩形区域像素值的方差以及矩形区域的宽高比这两个特征，来达到对接头点的识别。实验表明该算法能够有效地识别出接头点。

Identification Algorithm of Joints of Powerful Conveyor Belt

It's difficult to make a real time stoppage detection methods based on a point of joint for new type of powerful conveyor belt. The paper proposed novel method based on statistics and image processing technology for identifying joints of powerful conveyor belt of new type. First, this algorithm regards every line of the image as the overall sample. Then it calculates the degree of smoothness of the pixel according to each line's mean value and every pixel' variance of the line. On the basis of threshold of smoothness to find point of joint area can form a rectangle zone to apply erosion and dilating. For recognition joint, we can use two features that variance in this rectangle zone and width-height ratio for this rectangle zone. Experimental results show that this algorithm can better identify joints of powerful conveyor belt.