A Novel Wood Log Measurement Combined Mask R-CNN and Stereo Vision Camera

• Published in: Forests Vol. 14; no. 2; p. 285
• Main Authors: Yu, Chunjiang, Sun, Yongke, Cao, Yong, He, Jie, Fu, Yixing, Zhou, Xiaotao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Algorithms; Analysis; binocular stereo camera; Binocular vision; Cameras; Contours; CT imaging; Customs; Diameters; Errors; Health aspects; instance segmentation; Localization; Lumber; Mask R-CNN; Measurement methods; Methods; Neural networks; Production processes; Remote sensing; Search algorithms; Segmentation; Trees; Unmanned aerial vehicles; Wood; wood log measurement; China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14020285

Wood logs need to be measured for size when passing through customs to verify their quantity and volume. Due to the large number of wood logs needs through customs, a fast and accurate measurement method is required. The traditional log measurement methods are inefficient, have significant errors in determining the long and short diameters of the wood, and are difficult to achieve fast measurements in complex wood stacking environments. We use a Mask R-CNN instance segmentation model to detect the contour of the wood log and employ a binocular stereo camera to measure the log diameter. A rotation search algorithm centered on the wood contour is proposed to find long and short diameters and to optimal log size according to the Chinese standard. The experiments show that the Mask R-CNN we trained obtains 0.796 average precision and 0.943 IOUmask, and the recognition rate of wood log ends reaches 98.2%. The average error of the short diameter of the measurement results is 5.7 mm, the average error of the long diameter is 7.19 mm, and the average error of the diameter of the wood is 5.3 mm.