ResNet50-Driven Quality Inspection for Recorder Musical Instrument

The manufacturer of a recorder musical instrument requires high-quality product. The aim is to produce precise tones and an aesthetic look at customer satisfaction. A major challenge encountered by manufacturers is traditional visual inspection. Human error is a major factor, notably over extended work periods and the subjective judgment of quality control personnel. This paper reports on the development of a machine vision system for detecting abnormal patterns on the inner surface of a recorder musical instrument. An industrial-grade camera with a resolution of 1280 × 1024, paired with industrial lighting, was utilized. Due to its tube-shaped construction of the object, the bright-field imaging technique is applied to illuminate the interior. ResNet50 was selected as a feature extractor due to its balance between accuracy and efficiency. In addition, a Neural Network served as the classifier. A total of 1,118 images were collected as training data and 304 images as testing data. The training and testing data were separate sets that were taken independently, preventing any risk of data leakage. The test results indicated that the model performed exceptionally well in classification, achieving an accuracy of 95.7%, precision of 95.45%, sensitivity of 96.07%, and specificity of 95.36%. Moreover, the area under the curve of the Receiver Operating Characteristic (ROC AUC) score in test data reached 0.9906, reflecting the model's ability to separate features from the two classes. These findings suggest that the proposed method offers an alternative to subjective visual inspection. Future research may examine diverse deep learning architectures to further enhance performance while achieving faster classification.