Abstract
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 from images in the early stages. The TPMN model, being model-agnostic, effectively extracts and fuses low-level texture features while comprehensively perceiving multiscale features, yielding better results for packages with rich textures.
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TPMN defect detection network architecture.