Abstract
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.
Highlights
The 4th Industrial Revolution has stimulated the need for innovation in the manufacturing industry, and the intelligent manufacturing system is becoming an important issue [1,2,3,4,5]
We use the average value of the intersection of union (IoU) of this verification data as the accuracy index of the deep learning model
This paper introduced and implemented the process of segmenting the vent spew error, a type of tire failure error, through the four steps of (1) image input, (2) highlight images creation, (3) image stacking, and (4) image training
Summary
The 4th Industrial Revolution has stimulated the need for innovation in the manufacturing industry, and the intelligent manufacturing system is becoming an important issue [1,2,3,4,5]. The manufacturing paradigm is changing from the mass production of a small range of types to multi-products [6,7,8] For this reason, to adapt to the change in the manufacturing paradigm and meet the needs of consumers, the existing manufacturing process is developing to become more flexible. With the development of IoT technology, it has become possible to extract data from all manufacturing processes. Based on this vast amount of data, intelligent systems aim to improve productivity and energy efficiency and reduce defect rates [9,10,11,12]. Surface inspection is performed based on vision systems, such as ultraviolet, microscopy, RGB, and depth imaging [15,16,17,18], while inner structure inspection is performed by X-ray, 3D-CT, and ultrasound [19,20,21]
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