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Automotive adhesive defect detection based on improved YOLOv8

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Abstract

In automotive adhesive defect detection, manual inspection suffers from low efficiency and blind spots in human vision, which affects the performance of parts. Therefore, automated detection methods are particularly important. To address the issue of adhesive defects significantly impacting production during automated gluing processes, we propose an adhesive defect detection method for automotive applications based on the improved YOLOv8 (named YOLOv8n-SSE). First, we used the SSE (skip squeeze and excitation) attention mechanism in the backbone part to dynamically adjust the importance of different channels in our model and allow our model to selectively focus on important features. Then, the original bounding box loss function is replaced by the WIoU loss function. Experimental results demonstrate that this method improves the mAP50 of the original YOLOv8n by 3.25% and achieves an average detection speed of 7.9ms per image, equivalent to 126.58 frames per second (FPS), meeting the real-time defect detection requirements.

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Data Availability

The dataset we utilized cannot be made publicly available due to commercial contracts. While we are unable to provide open access to the dataset, we will furnish detailed descriptions of the dataset within reasonable bounds to assist other researchers in comprehending our research methods and findings.

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Funding

.This work was partly supported by Major science and technology projects of Jilin Provincial Department of science and technology (No.20210301038GX) and Open fund of Key Laboratory of symbolic computing and knowledge engineering of Ministry of education (No.93K172021K10).

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Author notes

  1. Qibo Sun and **aogang Dong contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics and Statistics, Changchun University of Technology, 2055 Yan’an Street, Changchun, 130000, Jilin, China

    Chunjie Wang, Qibo Sun, **aogang Dong & Jia Chen

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  1. Chunjie Wang
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  2. Qibo Sun
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  3. **aogang Dong
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  4. Jia Chen
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Contributions

Wang, Sun and Dong, Chen wrote the main manuscript text, Wang and Dong are responsible for obtaining data, Sun and Chen are responsible for the experiment. All authors reviewed the manuscript.

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Correspondence to Jia Chen.

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Wang, C., Sun, Q., Dong, X. et al. Automotive adhesive defect detection based on improved YOLOv8. SIViP 18, 2583–2595 (2024). https://doi.org/10.1007/s11760-023-02932-1

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