结合深度学习与Hough变换的等长原木材积检测系统

An equal length log volume inspection system using deep⁃learning and Hough transformation

针对成捆堆放的原木端面图像,利用机器视觉方法开发一个材积检测系统,算法的鲁棒性和系统应用的方便性是两个关键难点。为了解决上述问题,实现原木材积自动化检测,设计并开发了结合深度学习与Hough变换的等长原木材积检测系统。首先,在对训练样本做了旋转、调整曝光度、加噪声等提高模型鲁棒性的预处理后,使用YOLOv3⁃tiny卷积神经网络对原木端面图像进行目标检测,计算得到每一个原木端面对应的目标区域;其次,在对目标区域计算边缘并去除目标区域中心噪声边缘后,利用Hough变换圆检测算法计算原木端面轮廓的准确直径;最后,开发了操作简捷方便的用户界面,根据用户输入的一个原木轮廓径级校准信息和长度,即可实现图中所有原木的材积检测。本系统在多种原木端面图像上,包括端面完好、端面伐痕、端面霉变、环境复杂等情景进行实验验证,系统真检率为98.79%,误检率为0.602%。结果显示本系统在各种复杂原木端面图像上具有很好的鲁棒性,同时为了兼容深度学习算法与用户界面设计,本系统在PyQt5核心库上实现了操作界面,其操作简洁、使用方便。

Aiming at examining the end images of logs stacked in bundles,a volume inspection system is developed u⁃sing machine vision methods.The robustness of the algorithm and the convenience of system application are two key problems.In order to realize the automatic inspection of log volume and solve the above problems,an equal length log volume inspection system using deep learning and Hough transformation was developed.Firstly,after the pre⁃process⁃ing of the training samples such as rotating the image,adjusting the photo exposure ratio,adding noise to the image and so on to improve the robustness of the model,the YOLOv3⁃tiny convolutional neural network was used to detect the image of the log end face,and then obtain the target area corresponding to each log end face.Secondly,in order to calculate the accurate diameter of the log end profile from the target area obtained by YOLOv3⁃tiny,the Hough transform circle detection algorithm was applied on the target area of the original image,after removing the noise ed⁃ges in the center of the target area.Finally,an equal length log volume inspection system with a simple and conven⁃ient user interface was developed,which can output the volume of each log from the value of a real log diameter and the length entered by the user.It can realize the volume inspection of all logs in the image.The system was tested by many kinds of log end face images,including clean log end face image,log end face with some cut marks,log end face with some moldy,log end face image with complex environment,etc.The result showed that the algorithm pro⁃posed in this study had the true detection rate of 98.79%,and the false detection rate of 0.602%.The result confirmed that the system had good robustness in many kinds of complex log end face images.The results also showed that the system was very robust on various complex log end face images.On the other hand,for compatibility with deep learn⁃ing algorithm and user interface design,the operation interface of the system was implemented on the PyQt5 core li⁃brary,which is simple and easy to use.