An Automatic Method for Measuring Strands'Orientation Angles Based on Convolutional Neural Network
Oriented strand board(OSB)is a popular wood-based panel product that has gained widespread application in construction,furniture,and other industries due to the high strength,durability,and cost-effectiveness.The basic component of OSB is large strand that is arranged in a specific orientation and bonded together using adhesives to form a strong and durable panel product.The strand's orientation angle is a key factor that affects the mechanical properties of OSB.The effective collection of information on strands'orientation angles is important for optimizing the production process and improving product performance.In this study,a method was developed for automatically measuring the strand's orientation angle with computer vision and machine learning techniques.The method is based on the convolutional neural network YOLOv5 and the minimum outer rectangle algorithm,which can accurately and efficiently identify and measure the strand's orientation angle during the stranding process.The study evaluated the performance of YOLOv5,the accuracy of automatically calculating the strand's orientation angle,and the time efficiency of the automatic strand's orientation angle acquisition.The results of the study showed that the YOLOv5 model was effective in identifying the object from the images of the layered strands,with an accuracy,recall,and F1 value of 0.992,0.897,and 0.94.The model can effectively identify the layered strands.The orientation angles of strands measured by automatic and manual methods were strongly correlated(R2 = 0.99).There were no orientation angle algorithmic flaws.Furthermore,the method established in this study has high time efficiency,with a processing time of only 134.7 ms for each 640×640-pixel image.In conclusion,the method for automatically measuring the strand's orientation angle using YOLOv5 and minimum outer rectangle algorithm provides an efficient and accurate way to collect strands'orientation angle information during the stranding process,which is crucial for optimizing the production process and improving product performance.This research has important practical applications in the wood-based panel industry and provides a valuable reference for future research in this area.
万方数据
维普
