Comparative Evaluation of Pretrained Convolutional Neural Networks with Attention Enhancements for Defect Detection in Injection-Molded Products | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Evaluation of Pretrained Convolutional Neural Networks with Attention Enhancements for Defect Detection in Injection-Molded Products Chibueze Onukelobi, Kesiena Owebor, Okoi Obeten This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8193554/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent advances in machine and deep learning have paved the way for automated quality control in injection molding. However, existing approaches still face challenges such as insufficient performance evaluations, scarce training data, and limited generalization. In this study, a comparative evaluation of commonly used pretrained Convolutional Neural Networks, including InceptionV3, ResNet-101, ResNet-50, and VGG16, for defect detection was carried out. It was shown that, InceptionV3 outperformed the other models in test accuracy and convergence behavior, under consistent hyperparameter settings and fine-tuning strategies. Building on this Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) were integrated into InceptionV3, forming InceptionV3-CBAM-SE, to enhance feature representation. The proposed framework was tested on three diverse injection molding datasets to demonstrate generalized application and industrial adaptability. Compared to the baseline InceptionV3, the InceptionV3-CBAM-SE achieved accuracy gains in excess of 66%, 61.67%, and 47.62% on the three diverse injection molding datasets, reaching final accuracies of 83%, 97%, and 93%. These results demonstrate the effectiveness of the proposed model in improving defect detection performance in injection molded products. Industrial Engineering Advanced Manufacturing Computer vision Defect Detection Injection molding Squeeze-and-Excitation Convolutional Block Attention Module Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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