Automatic Detection of Round Timber in Digital Images Using Random Decision Forests Algorithm

Chiryshev, Yurii V.; Kruglov, Artem; Atamanova, Anastasia S.

doi:10.1145/3232651.3232667

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Besides, [21] automatically detected logs by combining the Histogram of Gradients (HoG) features with a Random Decision Forest scheme. However, the log shape is constrained to highly round-shaped logs, and the HoG features are significantly affected by illumination conditions.…”

Section: B Computer Vision Methodsmentioning

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: B Computer Vision Methodsmentioning

confidence: 99%

Research on Volume Measurement of Logs Based on Embedded Application

Zheng

Zhang

et al. 2023

IEEE Access

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“…An Alternative algorithm in the literature is based on a combination of HOG and random forest decision algorithm and can find log edges in bundle images taken from the rear [16], [17]. This method shows better results than the previous one because it uses a machine learning technique, the random forest, instead of simple computer vision techniques.…”

Section: A) Literature Reviewmentioning

confidence: 99%

Vision-Based System for Measuring the Diameter of Wood Logs

Carratù

Gallo

Liguori

et al. 2023

IEEE Open J. Instrum. Meas.

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Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, the paper proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of Convolutional Neural Network (CNN) named YOLO (You Only Look Once) and a Direct Linear Transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today's methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.

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“…For instance, annual tree ring width is an indication to wood mechanical properties [2]. A lot of techniques have been proposed to segment CS on timber trucks or log stacked in a pile [3]- [5]. Samdangdech et al [4] used neural network to segment log-end on timber trucks.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends

Decelle¹,

Jalilian²

2020

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS)

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In this paper, wood cross-section (CS) segmentation of RGB images is treated. CS segmentation has already been studied for computed tomography images, but few study focuses on RGB images. CS segmentation in rough log ends is an important feature for the both assessment of wood quality and wood traceability. Indeed, it allows to extract other features like pith, eccentricity (distance between the pith and the geometric centre) or annual tree rings which are related to mechanical strength. In image processing, neural networks have been widely used to solve the problem of objects segmentation. In this paper, we propose to compare different state-of-the-art neural networks for CS segmentation task. In particular, we consider U-Net, Mask R-CNN, RefineNet and SegNet. We create an imageset which has been split into 6 subsets . Considered neural networks have been trained on each subset in order to compare their performance on different type of images. Results show different behaviors between neural networks. On the one hand, overall U-Net learns better on small dataset than the others. On the other hand, RefineNet learns well on huge dataset. While SegNet is less efficient and Mask R-CNN does not provide a detailed segmentation. This offers a preliminary result on neural network performances for CS segmentation.

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Automatic Detection of Round Timber in Digital Images Using Random Decision Forests Algorithm

Cited by 7 publications

References 13 publications

Research on Volume Measurement of Logs Based on Embedded Application

Research on Volume Measurement of Logs Based on Embedded Application

Vision-Based System for Measuring the Diameter of Wood Logs

Neural Networks for Cross-Section Segmentation in Raw Images of Log Ends

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