A Defect Detection Method for Functional Membranes in Flexible Sensors for Vibration Monitoring During Glass...

Vibration monitoring of glass substrate transfer systems is crucial for ensuring the stable operation of Flat Panel Display (FPD) manufacturing equipment. Fabrication defects in the functional nanofiber membrane of flexible vibration sensors can significantly degrade sensing performance and lead to inaccurate monitoring results. To address the challenge of achieving an effective balance between detection accuracy and inference efficiency in such defect-dense scenarios characterized by large variations in defect scale, this paper proposes a novel defect detection model, termed MA-YOLO. The proposed model incorporates four key architectural enhancements: the Multi-level Bidirectional Feature Aggregation Network (MLBAN), the Multi-Receptive Field Adaptive Fusion Module (MRAF), the Morphology-Adaptive Feature Extraction Module (MA-C2f), and the Interactive Dynamic Decoupling Head (IDDH). These components collaboratively improve defect feature extraction, multi-scale feature fusion, and localization performance while maintaining a lightweight architecture and high inference speed. Experimental results on a self-constructed defect dataset demonstrate that MA-YOLO achieves a mean Average Precision (mAP@0.5) of 91.9%, which is a 3.1 percentage point improvement over the baseline model. Moreover, with only 9.15 million parameters and an inference speed of 119.05 FPS, the proposed model exhibits superior overall performance compared with several mainstream and state-of-the-art object detection methods.