Defect Inspection in Tire Radiographic Image Using Concise Semantic Segmentation

Zheng, Zhouzhou; Zhang, Sen; Yu, Bin; Li, Qingdang; Zhang, Yan

doi:10.1109/access.2020.3003089

Cited by 45 publications

(28 citation statements)

References 32 publications

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“…Over the past few years, many researchers have applied neural networks to nondestructive testing of tires. In [ 10 ], the authors propose a tire defect detection method based on a concise semantic segmentation network. They propose segmentation networks and compact convolutional neural networks for tire defect detection, resulting in smaller model size and faster detection.…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

Yang

Jiao

Wang

et al. 2022

Sensors

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With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.

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Section: Background and Related Workmentioning

confidence: 99%

“…Many scholars have applied neural networks to traffic cars [ 7 , 8 , 9 ]. Specific to tire production scenarios, there are more and more tire defect detection methods based on deep learning [ 10 , 11 , 12 ]. This detection technique is more effective than manual detection.…”

Section: Introductionmentioning

confidence: 99%

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

Yang

Jiao

Wang

et al. 2022

Sensors

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show abstract

“…Deep learning methods applied to error detection are composed of (1) classification and (2) segmentation methods. The same defects on the tire surface follow an atypical shape [34,35]. For example, in the case of a vent spew error, it is difficult to have the same shape because the type of tire, the location of the error, the size of the tire, and the length of the protruding hair are different.…”

Section: Deep Learning Segmentationmentioning

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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“…Non-destructive testing (NDT) is the main development direction of tire inspection technology (TIT). Common tire NDT include: phase-shift shearing speckle interference technology [2], X-ray imaging testing technology [3], ultrasonic testing technology [4], electrical Pulse detection technology [5], electromagnetic wave technology [6], image recognition technology [7,8]. The TIT is mainly based on defect detection (damage, air bubbles, sub-port exit, lack of glue, exposed lines, cracks, etc.)…”

Section: Introductionmentioning

confidence: 99%

Research on Tire Groove Depth Detection System based on MVR

Qian¹,

Xingwen²,

Zhu³

et al. 2022

Preprint

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The change of tire groove depth will have a huge impact on tire performance, and the use of excessively worn tires is not conducive to the driving safety of automobiles. Tire groove depth detection has become one of the annual inspection items of automobiles, but the research on its related detection technology is still relatively backward. Based on the principle of monocular vision ranging (MVR), image processing technology and cloud platform technology, this paper develops a tire groove depth detection system, which realizes non-destructive detection of tire groove depth. In addition, the system uses the cloud platform to store the test results, and builds a multi-level data management system, allowing car owners to keep track of the tire wear status and historical changes, which is of great significance to ensuring driving safety.

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Defect Inspection in Tire Radiographic Image Using Concise Semantic Segmentation

Cited by 45 publications

References 32 publications

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

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

Research on Tire Groove Depth Detection System based on MVR

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