基于纹理主、旁瓣特征的雪糕棒裂缝缺陷检测

裂缝是雪糕棒表面一种严重缺陷,对雪糕棒的加工和使用影响极大,然而部分又细又浅的裂缝与雪糕棒表面的木纤维纹络具有诸多相似之处,使得当前检测算法提取效果不佳。针对该问题,在对裂缝纹理及木纤维纹络的特征进行详细分析的基础之上,提出了一种基于纹理主瓣和旁瓣灰度特征相结合的检测方案。首先建立纹理主瓣和旁瓣灰度特征提取基本模型;然后提取雪糕棒表面头部全部纹理的边缘;接着,根据建立的模型提取上一步骤所得各边缘相应纹理的主瓣和旁瓣灰度特征量,并根据主瓣特征量的数值大小初步锁定其中属于裂缝纹理的候选边缘(其中包括全部的裂缝边缘和部分木纤维纹络边缘);最后,通过旁瓣特征量与预设阈值的数值关系识别出上一步骤候选边缘中的裂缝纹理边缘,从而实现裂缝缺陷的检测。在自建图库SUT-I3上进行了测试,结果显示所提方法在裂缝缺陷漏检率为0的前提下,其误检率低至6.07%,相对于其他雪糕棒或木材表面裂缝检测方法其误检率最少降低了9.29%,表明了所提方法的高效性,具有实际应用价值。

Detection of ice cream stick crack defects based on texture mainlobe and sidelobe features

The crack is a serious defect on the ice cream stick surface, which affects the processing and usage of the ice cream stick seriously. However, certain thin and light cracks are similar to the wood fiber textures on the ice cream stick surface, which results in the poor extraction performance of present detection algorithms. Aiming at this problem, a detection scheme based on the combination of the mainlobe and sidelobe texture gray features is proposed on the basis of detailed analysis of crack texture characteristic and wood fiber texture characteristic. Firstly, the basic model of the texture mainlobe and sidelobe gray characteristic extraction is built. Secondly, the whole texture edges on the ice cream stick head are extracted. Then, the mainlobe and sidelobe texture gray features of corresponding textures of the edges extracted in the previous step are extracted according to the established model, and the candidate crack edges (containing all the crack edges and certain wood fiber texture edges) are preliminarily determined according to the mainlobe characteristic quantity size. Lastly, the crack texture edges are recognized from the candidate edges derived from the previous step according to the numerical relationship of the sidelobe characteristic quantity and the preset threshold value; and the crack defect detection is achieved. The algorithm was tested on the self built image database SUT I2. The results show that the crack defect FAR of this method is as low as 6.07 percent on the premise that the missing detection rate is 0; the missing detection rate is decreased by at least 9.29 percent compared with other ice cream stick or wood fiber surface crack detection algorithms, which indicates that the proposed crack detection scheme is superior and has actual application value.