Evaluation of Superficial Damage of Mechanical Components through Digital Image Analysis Using Deep Learning Framework

Mechanical component failure often heralds superficial damage indicators such as color alteration due to overheating, texture degradation like rusting or false brinelling, spalling, and crack propagation. Conventional damage assessment relies heavily on visual inspections performed by technicians, a practice bogged down by time constraints and the subjective nature of human error. This research paper delves into the integration of deep learning methodologies to revolutionize surface damage evaluation, addressing significant bottlenecks in diagnostic precision and processing efficiency. We detail the end-to-end process of developing an intelligent inspection system: selecting appropriate deep learning architectures, annotating datasets, implementing data augmentation, optimizing hyperparameters, and deploying the model for widespread user accessibility. Specifically, the paper highlights the customization and assessment of state-of-the-art models, including EfficientNet B7 for multilabel classification and prominent object detection framework such as YOLO. Our results demonstrate the feasibility of automating damage detection, potentially transforming maintenance routines and reliability in mechanical settings. Follow-up research is planned to refine these methodologies, paving the way for a production-ready model that will further enhance the reliability and efficiency of mechanical component maintenance.