基于机器视觉的木制压舌板裂纹自动拣选系统

针对压舌板表面裂纹缺陷的在线检测及劣品剔除问题,提出一种基于机器视觉的自动化检测系统。 首先基于对压舌板 及其裂纹特征的分析,设计了包含两组视觉检测机构的硬件装置。 该装置以链齿型传送带为基底,作为压舌板基本传送机构; 设计了链齿型传送带与反射型光电接近开关的特定装配模式,用于产生脉冲、提供系统时序;建立了基于多级缓存机制的系统 控制架构,协同调配时序脉冲触发、调用及使能各个硬件部件。 裂纹检测算法方面,采用一种基于方向空间显著性的方法。 首 先经 OTSU 算法、面积筛选以及形态学运算等预处理定位压舌板区域;然后基于方向空间显著性提取裂纹特征点,进而基于双 阈值连接限制生成候选裂纹线条;最后基于延展角度、起始位置等多维条件约束精确识别裂纹。 在实际生产现场对本文系统性 能进行了测试,结果显示,在检测效率为 11 支/ 秒的前提下,误检率低至 4. 17%,漏检率为 2. 68%,与当前人工检测方法相比分 别降低 6. 66%和 5. 36%,表现出优越的性能,具有较强的实际应用价值。

Automated picking system of wooden cracked tongue spatula based on machine vision

An automated detection system based on machine vision is proposed for the task of on-line detection of the crack defects on tongue spatula surface and the removal of inferior products. Firstly, based on the analysis of tongue spatula and its crack feature, a hardware device consisting of two groups of visual detection mechanisms is designed. The device is based on the chain-type conveyor belt as the basic transmission mechanism of the spatula. The specific assembly mode of chain-type conveyor belt and reflective photoelectric proximity switch is proposed to generate pulses and provide the timing sequence of the system. Based on multilevel caching mechanism, the system control architecture is designed for the collaborative allocation of the timing pulse triggering and the calling and enabling of each hardware component. In the aspect of crack detection algorithm, a method based on direction-space significance is adopted. Firstly, the preprocessing of OTSU algorithm, area screening and morphological operation is used to locate the spatula region. Then the crack feature points are extracted based on the direction-space significance. Furthermore, the candidate crack lines are generated based on the double threshold connection restriction. Finally, the cracks are accurately identified based on the characteristics of elongation angle, starting position and so on. The system performance is tested on the actual production site. The result shows that with the detection efficiency of 11 sticks per second, false positive rate (FPR) is as low as 4. 17% and false negatives rate (FNR) is 2. 68%, which are reduced by 6. 66% and 5. 36% respectively compared with the current manual detection method. It shows superior performance and has strong practical application value.