Application of YOLO and ResNet in Heat Staking Process Inspection

Hail Jung () and Jeongjin Rhee
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Hail Jung: Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
Jeongjin Rhee: Data Science Group, INTERX, Seoul 08503, Republic of Korea

Sustainability, 2022, vol. 14, issue 23, 1-14

Abstract: In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework, and a classification algorithm, residual network (ResNet), to a real heat staking process image dataset. The study leverages the advantages of YOLO models and ResNet to increase the overall efficiency and accuracy of detecting heat staking points from door trim images and classify whether the detected heat staking points are defected or not. The proposed model achieved high accuracy in both object detection (mAP of 95.1%) and classification (F1-score of 98%). These results show that the developed deep learning models can be applied to the real-time inspection of the heat staking process. The models can increase productivity and quality while decreasing human labor cost, ultimately improving a firm’s competitiveness.

Keywords: object detection; classification; deep learning; Artificial Intelligence; heat staking process; manufacturing industry (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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