TIANet: A Defect Classification Structure Based on the Combination of CNN and ViT

Defect detection plays a crucial role in ensuring product quality. However, accurate and effective defect detection remains a challenge due to the specific features inherent in defect images, including scale and shape variations. We propose a new defect classification structure—TIANet—which includes a local feature extraction module (ISD), a global feature extraction module (DSViT), and an atrous spatial pyramid pooling (ASPP) module. Among them, ISD is composed of inverted residual structure fusing extrusion and incentive attention mechanism and path discarding mechanism to realize the extraction of local features and the learning of complex patterns. DSViT is composed of Vision Transformer and deep separable convolution, which realizes the extraction of global features and fuses them with local features to ensure the accurate feature expression of defects with similar backgrounds. ASPP enhances the multi-scale feature extraction ability and contextual information capture ability of the model and effectively perceives defects of different shapes and scales. Experimental verification on the glass bottle dataset shows that it performs well in the defect classification task of national standard white glass bottle, with an accuracy rate of 95.714%. Compared with the typical network models Vision Transformer and MobileNetV3, TIANet shows significant advantages, which verifies its effectiveness and superiority in the defect classification of glass bottles.