Data Augmentation and Layered Deformable Mask R-CNN-Based Detection of Wood Defects

Li, Dongjie; Li, Dongjie; Baogang, Wang; Zhong, Weifeng; Wang, Hongmin

doi:10.1109/access.2021.3101247

Cited by 20 publications

(7 citation statements)

References 40 publications

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“…For instance, Deng et al applied WGAN‐GP to data augmentation for facial expression recognition, improving the accuracy of recognizing facial expressions from multiple angles (Gao et al, 2023). Li et al used CycleGAN to exchange textures and colors of three types of wood (poplar, birch, and pine), and generated images with cracks and worms, addressing the issue of imbalanced defect distributions and enhancing the accuracy of wood defect detection and segmentation (Li et al, 2021). These studies demonstrate the feasibility of applying GANs to visual recognition and detection tasks.…”

Section: Methodsmentioning

confidence: 99%

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Zhu,

Wang,

et al. 2023

J Food Process Engineering

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The presence of corn unsound kernels, one of the world's major food crops, could have a significant impact on the food industry and the food supply. The traditional method of detecting unsound corn kernels by hand during grain acquisition has many drawbacks, and computer vision‐based detection methods have become mainstream. In this paper, a corn unsound kernel detection algorithm based on generative adversarial network (GAN) sample enhancement and an improved lightweight network is introduced. The article first builds a corn unsound kernel image acquisition platform and makes a dataset by preprocessing and segmenting the collected corn seed cluster images with the improved concave point segmentation algorithm. Then, to increase the diversity and number of datasets, the StyleGANv2 network was improved to generate corn‐unsound kernel images with diverse features. Finally, to meet the demand for lightweight detection, the MobileVit network was optimized to improve the network's recognition accuracy, which reached 96.2%. The article verifies the effectiveness and superiority of the proposed algorithm through experiments.

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Section: Methodsmentioning

confidence: 99%

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Zhu,

Wang,

et al. 2023

J Food Process Engineering

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“…A generative adversarial network (GAN) is proposed to exaggerate the small defects within the images and also expand the defected samples [29]. Another GAN-based approach, cycle GAN, takes pairs of defect images to exchange their colors and textures to generate new defective data without changing the distribution of color and grain in the dataset [30].…”

Section: B Vision Based Defect Detectionmentioning

confidence: 99%

An Automatic Defect Detection System for Synthetic Shuttlecocks Using Transformer Model

Lin

Hsieh

2022

IEEE Access

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With an estimation of 220 million people playing badminton on a regular basis, it was particularly popular in Asia but has growing popularity in different regions of the world. The demands of the relevant products, such as shuttlecocks and rackets, are also increasing in the sports industry. Synthetic shuttlecock, produced to offer similar experience and feel as feather shuttlecocks to players, is a more economical alternative to feather shuttlecocks. In addition to maintaining high throughput production for synthetic shuttlecocks with cost reduction, a more substantial improvement in quality control is desired as well. Since the defect detection of synthetic shuttlecocks is a challenging task, it heavily relies on human visual inspection at present. The existing manual quality-inspection process is not only error-prone but also considerably less efficient. In this paper, we propose an intelligent system to overcome these difficulties and bridge the gap between research and practice. Two cylinder grippers are designed to automatically deliver the shuttlecocks, a camera is used for capturing images and an end-to-end objection detection approach based on the Transformer model is investigated to recognize defects. Empirical results show that the proposed system obtains encouraging performance with AP 50 value of 87.5% and outperforms other methods. Ablation studies demonstrate that our approach can considerably boost the detection performance of synthetic shuttlecocks. Moreover, the processing speed is much faster than human operators and suitable for industrial applications. INDEX TERMSSynthetic shuttlecocks, defect detection, intelligent system, transformer model, cylinder gripper. He was a Postdoctoral Research Fellow with Tunghai University, Taiwan, in 2018, where he is currently an Assistant Professor with the Master Program of Digital Innovation. His research interests include natural language processing, artificial intelligence, and machine learning. HAN-YI HSIEH received the B.E. and M.S. degrees in computer science from Tunghai University, Taichung, Taiwan, in 2013 and 2017, respectively. He is currently pursuing the Ph.D. degree in computer science with the

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“…With the wide application of deep learning-based algorithms in the eld of machine vision, especially in the eld of target recognition and defect detection, a practical solution is provided to address the above challenges. Many Frameworks, based on different kinds of CNN models, such as CNN [8], SSD [9], Faster-CNN [10,11], Mask R-CNN [12,13], etc., have been derived to be applied to wood surface defect recognition. YOLO family of models [14,15] and their improved versions [16,17] as well.…”

Section: Introductionmentioning

confidence: 99%

WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing

Wang,

Zhang,

Liang

et al. 2024

Preprint

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Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark removal processing named WPS-dataset is proposed in this study, which consists of 4863 images. We designed an image acquisition device and assembled it on a bark removal equipment to capture images in real industrial settings. We evaluated the WPS-dataset using six typical segmentation models. The models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. We believe that our dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field.

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Data Augmentation and Layered Deformable Mask R-CNN-Based Detection of Wood Defects

Cited by 20 publications

References 40 publications

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

Detection of corn unsound kernels based on GAN sample enhancement and improved lightweight network

An Automatic Defect Detection System for Synthetic Shuttlecocks Using Transformer Model

WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing

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