A Novel Method to Inspect 3D Ball Joint Socket Products Using 2D Convolutional Neural Network with Spatial and Channel Attention

Sensors

Eungsoo Kim¹  Anvarjon Tursunov²  Bekhzod Mustafaev³  Sungwon Kim⁴ 

[1] Department of Artificial Intelligence Convergence Engineering, Busan University of Foreign Studies, Busan 46234, Korea;Department of Software, Sejong University, Seoul 05006, Korea;ICT Creative Convergence Department, Busan University of Foreign Studies, Busan 46234, Korea;Vazil Company Co., Ltd., Busan 46918, Korea;

关键词: quality inspection;    attention mechanism;    convolutional neural networks;    deep learning;   

DOI  :  10.3390/s22114192

来源: DOAJ

【 摘 要 】

Product defect inspections are extremely important for industrial manufacturing processes. It is necessary to develop a special inspection system for each industrial product due to their complexity and diversity. Even though high-precision 3D cameras are usually used to acquire data to inspect 3D objects, it is hard to use them in real-time defect inspection systems due to their high price and long processing time. To address these problems, we propose a product inspection system that uses five 2D cameras to capture all inspection parts of the product and a deep learning-based 2D convolutional neural network (CNN) with spatial and channel attention (SCA) mechanisms to efficiently inspect 3D ball joint socket products. Channel attention (CA) in our model detects the most relevant feature maps while spatial attention (SA) finds the most important regions in the extracted feature map of the target. To build the final SCA feature vector, we concatenated the learned feature vectors of CA and SA because they complement each other. Thus, our proposed CNN with SCA provides high inspection accuracy as well as it having the potential to detect small defects of the product. Our proposed model achieved 98% classification accuracy in the experiments and proved its efficiency on product inspection in real-time.

【 授权许可】

Unknown