# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 4 Conclusions  
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. Detecting a vent spew error in which rubber hairs protrude more than 2 mm from the tire tread is an inefficient task because it is necessary to measure the protrusion lengths for the number of rubber hairs  
Figure 15: Training/validation loss for the (**a**) original and the (**b**) proposed method training.  
existing in one tire. Therefore, in this paper, the vent spew error was easily and quickly detected by acquiring the height value of the tire surface with a 3D camera, and training a deep learning network with it during the tire inspection process. However, since the height values measured by the 3D camera were concentrated in a narrow range, the performance when training only the depth image of one channel was not good.  
Therefore, in this paper, based on the existing data, it was easy to train for other errors in the future, so additional data of the (1) histogram equalization, and (2) histogram heatmap images were generated and included in the training data. In this proposed method, the original data were not lost.  
The existing one channel depth image training results were compared with the results trained through the proposed process for experiments and evaluations. As a result of the experiment, both the mean IoU and the vent spew error detection rate increased by about 7-10%, respectively. The epoch was shortened by 80% during training, until the average IoU remained stable at 61%. However, there was limited accuracy to classify the atypical rubber hairs that protrude more than 2 mm from the tire surface based on the depth image with only a tiny difference in one-dimensional height information. This limitation is explained by a graph in which the training and validation losses do not converge but diverge. In addition, since the tire tread photographed with a 3D camera has a curved shape, it is necessary to correct the height of the center part and the edge part of the tire. To solve these limitations, we plan to research more accurate vent spew error measurement in the future: methods, standards, and depth image generation.  
The introduction of artificial intelligence technology into the visual inspection stage, which can be the last stage of the manufacturing process, is expected to greatly increase productivity by eliminating inspection errors that arise because each operator has different skill levels and error evaluation standards.  
Conceptualization, D.K. and H.K.; methodology, D.K.; software, D.K. and H.K.; validation, S.K., W.L. and Y.B.; formal analysis, J.P.; investigation, D.K. and S.K.; resources, W.L.; data curation, D.K.; writing--original draft preparation, D.K.; writing--review and editing, S.K.; visualization, D.K.; supervision, J.P.; project administration, Y.B.; funding acquisition, Y.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and ICT (MSIT), Korea, through the Grand Information Technology Research Center Support Program supervised by the Institute for Information and communications Technology Planning and Evaluation (IITP) under Grant IITP-2020-0-101741.  
Not applicable.  
The data availability statements are listed as follows: the experiment results data used to support the findings of this study are partly available from the corresponding author upon request.  
The authors declare that they have no conflict of interest.