Enhancing quality inspection in automotive manufacturing through deep learning and transfer learning

Ensuring quality control and accurate defect detection are paramount in the automotive manufacturing industry. Nevertheless, manual and automated optical inspection approaches have encountered limitations in efficiently addressing this area. To overcome these challenges, automotive companies are actively investigating the utilization of deep learning models to automate and optimize quality control processes. This paper focuses on the development of image classification models with the specific objective of accurately categorizing industrial components as acceptable or defective. The targeted manufacturing processes include terminal crimps, gluing the cap of coils, soldering pins and inductors. Our approach leverages transfer learning to mitigate the limited data, utilizing ten state-of-the-art convolutional neural network models (CNNs) previously developed for the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC): DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50V2, ResNet101V2, ResNet152V2, VGG19, and Xception. The last layer of these models replaced to align with our dataset's classes, and various strategies such as freezing layers, fine-tuning, adding other layers, and retraining are employed. Finally, we evaluated our performances models using metrics such as accuracy, precision, recall, and ROC–AUC. The results demonstrate the efficiency of transfer learning in quality inspection classification, with DenseNet121 and VGG19 achieving accuracy and precision rates exceeding 99%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.