TPMN: Texture Prior-Aware Multi-Level Feature Fusion Network for Corrugated Cardboard Parcels Defect Detection

Surface defect detection is the task of identifying and localizing defects on the surface of an object, which is a widely applied task in various industries. In the logistics industry, logistics companies need to monitor the condition of goods for potential defects throughout the entire logistics process for effective logistics quality control. However, effective defect detection methods are still lacking for courier packages using corrugated cardboard boxes, which rely on judging whether deformation and leakage have occurred by examining areas on their surface with abundant texture. Specifically, the defect rate and supporting structure of the packages are influenced by temperature and humidity, and the openings and bends of defects are inconsistent. This results in defective packages having rich and non-uniform texture features. Moreover, convolutional neural networks struggle to effectively extract low-level semantic texture features of defects and perceive multi-level image features of packages. Considering the above challenges, we propose a novel texture prior-aware multi-level feature fusion network (TPMN). We first introduce prior knowledge and attention mechanisms to enable the neural network to focus on extracting low-level texture features from the image in the early stages. We also design a multi-level feature fusion method to integrate features from different levels, avoiding the gradual loss of low-level semantic information in CNN and enabling comprehensive perception of multi-level image features. To support further research, we contribute the cardboard-boxes-dataset, comprising 1210 images of packages. Experiments on this dataset showcase the superior performance of TPMN, even in few-shot learning scenarios, demonstrating its effectiveness in surface defect detection within the logistics and supply chain domains.