A Lightweight Adaptive Attention Fusion Network for Real-Time Electrowetting Defect Detection

Electrowetting display technology is increasingly prevalent in modern microfluidic and electronic paper applications, yet it remains susceptible to micro-scale defects such as screen burn-in, charge trapping, and pixel wall deformation. These defects often exhibit low contrast, irregular morphology, and scale diversity, posing significant challenges for conventional detection methods. To address these issues, we propose ASAF-Net, a novel lightweight network incorporating adaptive attention mechanisms for real-time electrowetting defect detection. Our approach integrates three key innovations: a Multi-scale Partial Convolution Fusion Attention module that enhances feature representation with reduced computational cost through channel-wise partitioning; an Adaptive Scale Attention Fusion Pyramid that introduces a dedicated P2 layer for micron-level defect detection across four hierarchical scales; and a Shape-IoU loss function that improves localization accuracy for irregular small targets. Evaluated on a custom electrowetting defect dataset comprising seven categories, ASAF-Net achieves a state-of-the-art mAP@0.5 of 0.982 with a miss detection rate of only 1.5%, while operating at 112 FPS with just 9.82 M parameters. Comparative experiments demonstrate its superiority over existing models such as YOLOv8 and RT-DETR, particularly in detecting challenging defects like charge trapping. This work provides an efficient and practical solution for high-precision real-time quality inspection in electrowetting display manufacturing.