Deep Learning Based Online Defect Detection Method for Automotive Sealing Rings  

作　　者：Jian Ge Qin Qin Jinhua Jiang Zhiwei Shen Zimei Tu Yahui Zhang 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第5期3211-3226,共16页计算机、材料和连续体(英文)

摘　　要：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 ConvolutionalNetwork(DCN)and Spaceto-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.

关 键 词：Deep learning automotive sealing ring defect detection 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]