TY  - EJOU
AU  - Ge, Jian 
AU  - Qin,  
AU  - Jiang, Jinhua 
AU  - Shen, Zhiwei 
AU  - Tu, Zimei 
AU  - Zhang, Yahui 

TI  - Deep Learning Based Online Defect Detection Method for Automotive Sealing Rings
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Deep learning; automotive sealing ring; defect detection

DO  - 10.32604/cmc.2025.059389