The study on tire tread depth measurement method based on machine vision

Wang, Xibo; Li, Aijuan; Qin-peng, CI; Shi, Meng; Jing, Tian-Long; Zhao, Wanzhong

doi:10.1177/1687814019837828

Cited by 16 publications

(9 citation statements)

References 8 publications

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“…Generally, the Euclidean distance and Mahalanobis distance are used to express similarity. Traditional measurement methods such as KNN are realized by simple nonparametric estimation, but the measurement method based on depth learning is also called depth measurement, which uses CNN's strong feature representation ability to measure high-dimensional space [ 17 , 18 ]. Currently, metrics-based learning, which is commonly used in classification tasks with fewer samples, is suitable for networks, prototype networks, correlation networks, and twin networks.…”

Section: Food Recognition Based On Deep Convolutional Neural Networkmentioning

confidence: 99%

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Wang

Deng

et al. 2021

Computational Intelligence and Neuroscience

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With the development of machine learning, as a branch of machine learning, deep learning has been applied in many fields such as image recognition, image segmentation, video segmentation, and so on. In recent years, deep learning has also been gradually applied to food recognition. However, in the field of food recognition, the degree of complexity is high, the situation is complex, and the accuracy and speed of recognition are worrying. This paper tries to solve the above problems and proposes a food image recognition method based on neural network. Combining Tiny-YOLO and twin network, this method proposes a two-stage learning mode of YOLO-SIMM and designs two versions of YOLO-SiamV1 and YOLO-SiamV2. Through experiments, this method has a general recognition accuracy. However, there is no need for manual marking, and it has a good development prospect in practical popularization and application. In addition, a method for foreign body detection and recognition in food is proposed. This method can effectively separate foreign body from food by threshold segmentation technology. Experimental results show that this method can effectively distinguish desiccant from foreign matter and achieve the desired effect.

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Section: Food Recognition Based On Deep Convolutional Neural Networkmentioning

confidence: 99%

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Wang

Deng

et al. 2021

Computational Intelligence and Neuroscience

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“…It is challenging to use a traditional computer vision algorithm to detect many errors occurring on the tire surface through simple height information. Because the tire's tread forms a finely curved shape, methods using conventional computer vision algorithms to address this error require curvature correction before error detection [33]. Additionally, to accurately detect an error occurring in a tire, it is necessary to be able to filter the noise of the height value and to be able to determine the shape of the information displayed by the height difference.…”

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

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“…The larger the kernel is, the wider the receptive field, and the richer the output information. Therefore, large kernels are suitable for extracting features from large regions [21][22][23].…”

Section: Improved U-netmentioning

confidence: 99%

A Deep Learning Model for Striae Identification in End Images of Float Glass

Jin¹,

Xu²,

Tong³

et al. 2020

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For float glass, there is a correlation between the striae in end image and the manufacturing process. If clearly understood, the correlation helps to optimize and fine-tune the manufacturing process of float glass. This paper attempts to extract the striae from the end image of float glass with deep learning (DL) neural network (NN). For this purpose, an image segmentation model was established based on improved U-Net, a fully convolutional network (FCN), and used to accurately divide the glass liquid on the end image into different layers. Firstly, the improved U-Net model was constructed to extract the striae from each liquid layer on the end image. Next, the activation function and convolutional mode of the improved U-Net model were optimized to enhance the segmentation accuracy and shorten the training/prediction time. Finally, the proposed model was tested on the float glass production line of Hebei CSG Glass Co., Ltd. The test results show that our model achieved an accuracy of 94%. The research findings lay a solid basis for striae identification on end image of float glass, and provide guidance for optimization and fine-tuning of float glass manufacturing process.

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The study on tire tread depth measurement method based on machine vision

Cited by 16 publications

References 8 publications

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

A Deep Learning Model for Striae Identification in End Images of Float Glass

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