Edge-glued wooden panel defect detection using deep learning

Chen, Lun-Chi; Pardeshi, Mayuresh Sunil; Lo, Win-Tsung; Sheu, Ruey-Kai; Pai, Kai-Chih; Chen, Chia-Yu; Tsai, Pei-Yu; Tsai, Yueh-Tiann

doi:10.1007/s00226-021-01316-3

Cited by 26 publications

(14 citation statements)

References 36 publications

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“…5 , its first advantage is high versatility. Generally, the data we deal with are multidimensional ordered data, and then due to the rapid development of big data, deep learning has been applied very well in various fields 23 . In addition to speech recognition and image classification, it also has very good performance in data mining and data processing and data prediction, so its versatility is high.…”

Section: Prediction Methods Of Slope Stability Coefficient Of Open Pi...mentioning

confidence: 99%

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Wang¹,

Zhang²,

Wang³

2023

Sci Rep

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The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.

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Section: Prediction Methods Of Slope Stability Coefficient Of Open Pi...mentioning

confidence: 99%

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Wang¹,

Zhang²,

Wang³

2023

Sci Rep

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“…This demonstrates the feasibility of applying the 'look-once' (YOLO) network to surface defect detection in wood panels. Chen et al [30] combined traditional preprocessing algorithms with neural networks to detect edge-gluing defects on bonded wood panels, achieving a 97% accuracy and a 90% recall rate. In summary, recent years have witnessed significant progress in research on surface defect detection algorithms for wood panels, driven by deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

An efficient and accurate surface defect detection method for quality supervision of wood panels

Yi,

Luo,

et al. 2024

Meas. Sci. Technol.

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The wood panel processing sector is integral to the landscape of industrial manufacturing, and automated detection of wood panel surface defects has become an important guarantee for improving the efficiency and quality of processing production. However, due to the diverse scales and shapes of wood panel surface defects, as well as their complex and varied colors and texture characteristics, the efforts to efficiently and accurately detect surface defects in wood panels through existing methods have fallen short. Therefore, the paper proposes an enhanced YOLOx-tiny deep learning network for wood panel surface defect detection. We introduce new modules MFFM and CFEM, instead of the original SPP and Bottleneck modules to enhance key feature extraction and reduce the number of computational parameters.The experimental results conducted on the self-constructed wood panel surface defects dataset show that the mAP of our proposed method is 95.01%, which is 9.58% higher than the original YOLOx-tiny network model, and the defects recall is 91.46%, which is 13.21% higher compared to the original network. Meanwhile, the method is able to reduce 12.22% of computational parameters and 12.67% of GFLOPs, which effectively improves the efficiency of the detection of surface defects on wood panels. In summary, the proposed intelligent surface defect detection approach for wood panels, which utilizes an enhanced YOLOx-tiny deep learning network, has yielded notable outcomes in enhancing both accuracy and efficiency. This method holds significant practical relevance for the wood panel manufacturing sector, offering the potential to enhance both production efficiency and quality. It also explores the automation and intelligent technology in the process of man-made board processing, which provides a valuable reference for the research in related fields.

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“…CA encodes channel relationships and long-term dependencies through the location information of data, which can be divided into two steps: Coordinate information embedding and Coordinate Attention generation. In Coordinate information embedding, global pooling is used to globally encode spatial information, considering that global pooling will compress global spatial information into channel descriptors and location information cannot be preserved in order for the attention module to capture remote spatial interactions with precise location information, CA borrows Squeeze from SE Block; see Equation (17). Additionally, decompose global pooling to derive a pair of direction-aware feature maps, thereby enhancing the network's precision in pinpointing the targets of interest.…”

Section: Principle Of Coordinate Attention Mechanismmentioning

confidence: 99%

“…Deep learning-based target detection algorithms contribute to various applications, including remote sensing image detection [ 13 , 14 , 15 ], defect detection [ 16 , 17 ], targets tracking [ 18 , 19 ], and face recognition [ 20 , 21 ]. Among them, the YOLO series of algorithms is the most widely used.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

Gao,

Ma,

Zhao

et al. 2023

Sensors

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Currently, aeroplane images captured by camera sensors are characterized by their small size and intricate backgrounds, posing a challenge for existing deep learning algorithms in effectively detecting small targets. This paper incorporates the RFBNet (a coordinate attention mechanism) and the SIOU loss function into the YOLOv5 algorithm to address this issue. The result is developing the model for aeroplane and undercarriage detection. The primary goal is to synergize camera sensors with deep learning algorithms, improving image capture precision. YOLOv5-RSC enhances three aspects: firstly, it introduces the receptive field block based on the backbone network, increasing the size of the receptive field of the feature map, enhancing the connection between shallow and deep feature maps, and further improving the model’s utilization of feature information. Secondly, the coordinate attention mechanism is added to the feature fusion network to assist the model in more accurately locating the targets of interest, considering attention in the channel and spatial dimensions. This enhances the model’s attention to key information and improves detection precision. Finally, the SIoU bounding box loss function is adopted to address the issue of IoU’s insensitivity to scale and increase the speed of model bounding box convergence. Subsequently, the Basler camera experimental platform was constructed for experimental verification. The results demonstrate that the AP values of the YOLOv5-RSC detection model for aeroplane and undercarriage are 92.4% and 80.5%, respectively. The mAP value is 86.4%, which is 2.0%, 5.4%, and 3.7% higher than the original YOLOv5 algorithm, respectively, with a detection speed reaching 89.2 FPS. These findings indicate that the model exhibits high detection precision and speed, providing a valuable reference for aeroplane undercarriage detection.

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Edge-glued wooden panel defect detection using deep learning

Cited by 26 publications

References 36 publications

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

An efficient and accurate surface defect detection method for quality supervision of wood panels

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

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