Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components

Small and Medium Enterprises (SMEs) and Micro, Small, and Medium Enterprises (MSMEs) contemplate integrating machine vision with high throughput manufacturing lines to ensure a consistent quality of standardized components. The inspection productivity can improve considerably by substituting machine vision with manual activities. The pre-trained Convolutional Neural Networks (CNNs) can facilitate enhanced machine vision capabilities compared to the rule-based classical image processing algorithms. However, the non-availability of labeled datasets and lack of expertise in model development restricts their utilities for SMEs and MSMEs. The present work examines the practicality of utilizing publicly available labeled datasets while developing surface defect detection algorithms using pre-trained CNNs considering case studies of typical machined components - flat washers and tapered rollers. It is shown that the publicly available surface defect datasets are ineffective for specific-case such as machined surfaces of flat washers and tapered rollers. The explicitly labeled image datasets can offer better prediction abilities in such cases. A comparative assessment of common pre-trained CNNs is conducted to identify an appropriate network while developing a surface defect detection framework for machined components. The common pre-trained CNNs VGG-19, GoogLeNet, ResNet-50, EfficientNet-b0, and DenseNet-201 showing prediction abilities for similar classification tasks have been examined. The pre-trained CNNs developed using explicit image datasets were implemented to segregate defective flat washers and tapered rollers as sample components manufactured by SMEs and MSMEs. The performance assessment was accomplished using parameters estimated from the confusion matrix. It is observed that EfficientNet-b0 outperforms other networks on most parameters, and it can be preferred while developing a surface defect detection algorithm. The outcomes of the present study form the basis for developing an integrated vision-based expert system for surface defect detection tasks.