Autonomous and Cost-effective Defect Detection System for Molded Pulp Products

Molded pulp products, such as dinnerware, containers, packaging boxes, etc., have gained increasing popularity due to their eco-friendly features. One critical step in their production process is detecting their defects. In this paper, we present an autonomous defect detection system for such products. In the system design, we face four challenges: first, molded pulp products come in various forms and sizes; second, defects are typically small and appear in different forms; third, detection must be fast enough to achieve desired high production rates; fourth, low cost is a key consideration in the pulp molding industry. To overcome these challenges, we have designed a defect detection system with an enhanced YOLOV5s + DeepLabV3Plus backbone and specific modules. Particularly, we design a lightweight YOLOV5s network with an attention mechanism to improve YOLOV5s' accuracy and speed, for roughly detecting and identifying the type and position of defects. We then utilize the DeepLabV3Plus segmentation model for precise detection. We deploy multiple cameras to handle the products of different sizes and forms, and design a spatial-information based method to eliminate the duplication in detection by different cameras. We have implemented our detection system in a real-world pulp molding manufacturing line, using cost-effective hardware. We have conducted extensive evaluation on our system, demonstrating that our system can meet molded pulp production requirements.