Dynamic Fuzzy-Based Cost Assignment Module for a Reinforced Nested Segmentation Model in Industrial Fault Identification

Automation of industrial processes can have a real impact on the improvement of production or greater control over the maintenance of tools necessary for its smooth operation. In particular, solutions based on computer vision have a number of applications here, both in the context of production optimization and fault prevention. In this work, we present a method for identifying defects in metal surfaces, which are components of many industrial systems. The proposed solution is based on a semantic segmentation approach using a nested encoderdecoder architecture. What distinguishes the method from others is the implementation of a dynamic fuzzy-based cost assignment module, which manipulates the cost function during training to enhance the obtained results. With the help of a properly prepared fuzzy controller, which verifies the course of model adaptation to a specific case, the strategy can outperform the basic approach.