Computed Tomography as a Tool for Quantification and Classification of Roundwood—Case Study

Gergeľ, Tomáš; Bucha, Tomáš; Gracovský, Radovan; Chamula, Martin; Gejdoš, Miloš; Veverka, Peter

doi:10.3390/f13071042

Cited by 5 publications

(3 citation statements)

References 25 publications

(27 reference statements)

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“…Therefore, measuring the log diameter is an important process, where log diameter subtle differences determine the grade of logs and affect the price and economic losses of the industry. Currently, in the production enterprises of logs [6], the monthly log diameter class is determined manually, with workers using various measuring tools, such as tape measure or caliper, to measure the length and diameter of logs in Figure 1. Then, these measurements are input into the volume calculation formula to calculate the actual volume of logs.…”

Section: Introductionmentioning

confidence: 99%

Research on Volume Measurement of Logs Based on Embedded Application

Zheng

Zhang

et al. 2023

IEEE Access

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Spurred by the worldwide concern for forest protection and the increased log sales, most countries have standardized log volume calculations to avoid excessive timber and protect buyers. However, log volume is currently manually measured, suffering from high labor costs, low measurement progress, and imposing significant measurement errors. Thus, automatically obtaining the volumetric data of logs is a convenient and quick solution. Therefore, this work proposes a Mask Region Convolutional Neural Networkbased (R-CNN) algorithm for Logs volume measurement, named Wood Mask (WM) R-CNN. Specifically, we employ the Res2Net structure as the backbone to obtain receptive fields that exceed the input feature size, thus improving our model's multi-scale information fusion ability. Additionally, WM R-CNN relies on the Path Aggregation Feature Pyramid Network's (PAFPN's) path enhancement structure, shortening the low-level feature map's propagation path and improving the wood contour segmentation accuracy. Extensive experiments on the Vehicle-mounted Dense Logs (VMDL) dataset demonstrate that WM R-CNN affords a highly appealing segmentation accuracy for small, medium, and large wood, improving the corresponding mAP indicators against current methods by 2.0%, 1.2%, and 4.4%, respectively. Furthermore, a quantitative method based on TensorRT compresses the proposed model to deploy the WM R-CNN to mobile embedded devices. However, to compensate for the quantization loss, we introduce the expansion convolution operation method to manipulate the mask map and control the volume calculation error of all logs on a vehicle within 1%. The experiments reveal that the proposed method offers an appealing performance, verifying the algorithm's effectiveness and implementation ability on mobile terminals.

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Section: Introductionmentioning

confidence: 99%

Research on Volume Measurement of Logs Based on Embedded Application

Zheng

Zhang

et al. 2023

IEEE Access

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“…Franco) logs, then detected and measured knots using the images obtained from these scans, and obtained good localization accuracy and measurement precision [13]. Gergel used CT techniques to scan the wood log of three species of wood: oak, beech, and spruce, reconstruct their three-dimensional structures, and then calculated their volumes using four methods, and find that a method named STN 480009 has the best performance that the error of less than 0.01 m 3 [14]. Although these methods achieve satisfactory detection accuracy, it is hard to be used widely because the measurement equipment is too large to be portable.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Cao³

et al. 2023

Forests

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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.

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“…The problem is the too narrow portfolio of these devices and, at the same time, their high acquisition costs. There are currently 14 such CT lines installed in the world, which are located primarily in wood processing industries where they enable the optimization of cutting and processing plans for raw-wood assortments [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The Accuracy of CT Scanning in the Assessment of the Internal and External Qualitative Features of Wood Logs

Gejdoš,

Gergeľ,

Michajlová

et al. 2023

Sensors

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The qualitative evaluation of harvested raw logs and sawlogs is mainly based on the quantitative and qualitative evaluation of the visible macroscopic features of the wood. Modern methods allow for the analysis of whole logs by means of computed tomography. These devices can analyze the internal qualitative features of wood that are not visible on the external structures of the logs. The aim of this work was to evaluate the detection accuracy of a CT-scanning device intended for scanning logs on the internal qualitative features of wood using model trunks. Two logs of beech and oak with a length of 4 m were selected for the analysis, based on availability. Qualitative features were identified through computed tomography scanning, visually identified on cut sections, and then manually measured in accordance with applicable legislation. Relatively good agreement was demonstrated for the detected features in terms of identifying their location (dimension in millimeters from the end of the log). For this parameter, the average differences were 0.90% on the beech log and only 1.21% on the oak log. Relatively high accuracy was shown via CT detection of qualitative features in the beech section (with average differences in dimensions of only 3.5%). In the case of the oak log, the dimensions of the quality features were significantly overestimated. These results indicate that CT scanning technology may have a problem with some hardwood species. It was primarily developed for coniferous tree species, and software algorithms are, therefore, not yet fully adapted to the precise detection of the dimensions of individual quality features. Despite the detected differences, it was confirmed that the CT technology of scanning harvested wood can have a fundamental impact on optimization procedures in the recovery and processing of wood. Renting a scanning line for a certain capacity of wood volume appears to be a deployment option for forestry operations and smaller wood processing operations. Thus, this technology can become an important factor in improving the economic evaluation of the final production of wood.

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Computed Tomography as a Tool for Quantification and Classification of Roundwood—Case Study

Cited by 5 publications

References 25 publications

Research on Volume Measurement of Logs Based on Embedded Application

Research on Volume Measurement of Logs Based on Embedded Application

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

The Accuracy of CT Scanning in the Assessment of the Internal and External Qualitative Features of Wood Logs

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