Open Access

ARTICLE

Deep Learning Based Online Defect Detection Method for Automotive Sealing Rings

Jian Ge¹, Qin Qin^1,*, Jinhua Jiang¹, Zhiwei Shen², Zimei Tu¹, Yahui Zhang¹

1 School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209, China
2 School of Electrical Engineering and Telecommunications, UNSW, Sydney, NSW 2052, Australia

* Corresponding Author: Qin Qin. Email:

Computers, Materials & Continua 2025, 83(2), 3211-3226. https://doi.org/10.32604/cmc.2025.059389

Received 06 October 2024; Accepted 24 February 2025; Issue published 16 April 2025

Abstract

Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality. Deep learning algorithms show promise in this field, but challenges remain, especially in detecting small-scale defects under harsh industrial conditions with multimodal data. This paper proposes an enhanced version of You Only Look Once (YOLO)v8 for improved defect detection in automotive sealing rings. We introduce the Multi-scale Adaptive Feature Extraction (MAFE) module, which integrates Deformable Convolutional Network (DCN) and Space-to-Depth (SPD) operations. This module effectively captures long-range dependencies, enhances spatial aggregation, and minimizes information loss of small objects during feature extraction. Furthermore, we introduce the Blur-Aware Wasserstein Distance (BAWD) loss function, which improves regression accuracy and detection capabilities for small object anchor boxes, particularly in scenarios involving defocus blur. Additionally, we have constructed a high-quality dataset of automotive sealing ring defects, providing a valuable resource for evaluating defect detection methods. Experimental results demonstrate our method’s high performance, achieving 98.30% precision, 96.62% recall, and an inference speed of 20.3 ms.

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Keywords

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