MBGB-detector: A multi-branch gradient backhaul lightweight model for mini-LED surface defect detection

Abstract

To meet the growing demand for lightweight models and rapid defect detection in mini-light emitting diode (LED) chip manufacturing, we developed a highly efficient and lightweight multi-branch gradient backhaul (MBGB) block. Based on the MBGB block, a mini-LED surface defect detector was designed, which included an MBGB network (MBGB-net) for the backbone and an MBGB feature pyramid network (MBGB-FPN) for the feature fusion networks. MBGB-net was introduced to reduce resource utilisation and achieve efficient information flow while enhancing defect feature extraction from mini-LED wafers. MBGB-FPN optimises the parameter utilisation, thereby reducing the demand for computational resources while maintaining, or even improving, the detection accuracy. Furthermore, a partial convolution module is integrated into the detection head to reduce the computational overhead and improve the detection speed. The experimental results demonstrated that the method achieved optimal performance in terms of both accuracy and speed. On the mini-LED wafer defect dataset, it achieved an mAP50 of 87.2% with only 9.3M parameters and 21.6G FLOPs, reaching an impressive FPS of 345.4. Furthermore, on the NEU-DET dataset, an mAP50 of 77.5% was achieved using the same parameters and FLOPs.