A computer vision system for recognition and defect detection for reusable containers

A computer vision system for recognition and defect detection for reusable containers

연구 분야: Artificial Intelligence

학회: Machine Vision and Applications

Small load carriers (SLCs) are standardized reusable containers used to transport and protect customer goods in many manufacturers. Throughout the life cycle of the SLCs, they will be collected, manually checked for defects (wear, cracks, and residue on the surface), and cleaned by specialized logistic companies. Human operators in small to medium-sized companies manually evaluate the defects due to the variety and degree of possible defects and varying customer needs. This manual evaluation is not scalable and prone to errors. This work aims to fill this gap by proposing a computer vision system that can recognize the SLC type for inventory management and perform defect detection automatically. First, we develop a camera portal, consisting of standard components, that capture the relevant surfaces of the SLC. A labeled dataset of 17,530 images of 34 different SLCs with their defect status was recorded using this camera portal. We trained a classification model (ConvNeXt) using our dataset to predict the different types of SLCs achieving 100% class prediction accuracy. For defect detection, we explore eight state-of-the-art (SOTA) anomaly detection models that achieved high rankings in the MVTec industrial anomaly detection benchmark. These models are trained using default hyperparameters and the two highest-scoring models were chosen and fine-tuned. The best-fine-tuned models based on “Area under the Receiver Operating Characteristic Curve (AUROC)” are PatchCore (0.811) and DRAEM (0.748). These results indicate that there is still potential for improvement in the automation of defect detection of SLCs.