A Fast Algorithm to Approximate the Pith Location of Rubberwood Timber from a Normal Camera Image

Kurdthongmee, Wattanapong; Suwannarat, Korakot; Panyuen, Praepaka; Sae-Ma, Naruedom

doi:10.1109/jcsse.2018.8457375

Cited by 8 publications

(25 citation statements)

References 6 publications

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“…We also evaluated the trained models using detection rate and average location error. The results of our study reveal that both DNN pith detectors are superior to the most recently proposed non-DNN algorithm (Kurdthongmee et al, 2018). This is because the non-DNN pith location algorithm relies heavily on the quality of the cross-sectional surface of parawood.…”

Section: Discussionmentioning

confidence: 80%

“…However, when the location error is considered, the YOLO pith detector outperforms the SSD MobileNet pith detector because it has half the average location error. Both DNN pith detectors significantly outperform the non-DNN algorithm (Kurdthongmee et al, 2018). Table 2 also shows the average detection times of all three algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In Figure 6, some examples of wood cross-sectional images along with the detected pith locations are illustrated. This figure compares the pith locations detected by the method of Kurdthongmee et al (2018), the YOLO pith detector, and the SSD MobileNet pith detector. It is clear that both DNN pith detectors detect piths very close to their ground truth locations.…”

Section: Resultsmentioning

confidence: 99%

“…Many studies have proposed using the geometric relationships of the annual rings within the cross-section of wood stems to locate the pith (Kr€ ahenbühl et al, 2012;Boukadida et al, 2012;Kurdthongmee et al, 2018;Kurdthongmee and Suwannarat, 2019). Once the boundary of an annual ring has been extracted from an image, image processing routines are used to create segments from neighbouring pairs of pixels that form the boundary.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

Kurdthongmee

2020

Heliyon

Self Cite

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The location of pith in a cross-sectional surface of wood can be used to either evaluate its quality or guide the removal of soft wood from the wood stem. There have been many attempts to automate pith detection in images taken by a normal camera. The objective of this study is to comparatively study the effectiveness of two popular deep neural network (DNN) object detection algorithms for parawood pith detection in cross-sectional wood images. In the experiment, a database of 345 cross-sectional images of parawood, taken by a normal camera within a sawmill environment, was quadrupled in size via image augmentation. The images were then manually annotated to label the pith regions. The dataset was used to train two DNN object detection algorithms, an SSD (single shot detector) MobileNet and you-only-look-once (YOLO), via transfer learning. The inference results, utilizing pretrained models obtained by minimizing a loss function in both algorithms, were obtained on a separate dataset of 215 images and compared. The detection rate and average location error with respect to the ground truth were used to evaluate the effectiveness of detection. Additionally, the average distance error results were compared with the results of a state-of-the-art non-DNN algorithm. SSD MobileNet obtained the best detection rate of 87.7% with a ratio of training to test data of 80:20 and 152,000 training iterations. The average distance error of SSD MobileNet is comparable to that of YOLO and six times better than that of the non-DNN algorithm. Hence, SSD MobileNet is an effective approach to automating parawood pith detection in crosssectional images.

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Section: Discussionmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

Kurdthongmee

2020

Heliyon

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“…Recently, there have been some efforts to develop pith detection methods on RGB images of cross sections from tree logs [12,13,19,21]. Contrary to CT images, RGB images exhibit disturbances like sawing marks, dirt or ambient light variations, which make the detection more challenging (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Pith Estimation on Tree Log End Images

Decelle¹,

Ngo²,

Debled-Rennesson³

et al. 2021

Reproducible Research in Pattern Recognition

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In this paper, we present an algorithm for pith estimation from digital images of wood cross-sections. The method is based on a probabilistic approach, namely ant colony optimization (ACO). After introducing the approach, we describe the implementation and the reproduction of the method linking to an online demonstration. Results show that the approach performs as well as state-of-the-art methods. The estimated pith is below 5mm from the ground truth. It is a fast method that could be used in real-time environment. This paper also gives the details about the intern parameter choice and shows how to use the C++ source code for testing, as well as provides limit cases of the proposed method and future improvements.

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Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation

Marichal,

Passarella,

Randall

2024

Lecture Notes in Computer Science

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A Fast Algorithm to Approximate the Pith Location of Rubberwood Timber from a Normal Camera Image

Cited by 8 publications

References 6 publications

A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

Pith Estimation on Tree Log End Images

Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation

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