Deep Learning-Based Visual Detection Technology for Metal Gear Surface Defects

Abstract

Due to complex background interference, a lack of real defect samples, and inconsistent evaluation of real-time deployment in metal gear surface defect detection, four representative studies were selected for comparison and four technical approaches were analyzed: lightweight detection, interference resistance, few-sample augmentation, and production-line application. The results show that lightweight networks can increase the speed of detection; attention mechanisms and multi-scale feature fusion help suppress interference from oil contamination, shadows, etc.; and generative models can address the shortage of defect samples. Insufficient coverage of real samples still affects the model's generalization. Although the actual production-line system meets the cycle-time requirements, its false-positive rate and stability in the face of changes have not yet been optimized. Based on the above analysis, this paper proposes expanding the collection of real samples across different gear models and production batches, strengthening tests under complex imaging conditions, standardizing the evaluation of complete inspection time, and conducting comprehensive assessment using both false-positive and false-negative rates. The above recommendations provide a reference for the engineering application of visual inspection systems for metal gear surface defects.