Micro LED defect detection with self-attention mechanism-based neural network

Abstract

We propose a method utilizing a YOLO detector for the precise localization of defective chips and the identification of defect types within multi-scale multi-target images. To address the challenge of optimizing training costs and enhancing model generalization, we introduce an end-to-end deep neural network, CM-YOLOv5, specifically designed for chip detection. We incorporate a novel bottleneck layer, MA-CSP, in conjunction with Multi-Head Self-Attention mechanism (MHSA). Additionally, we propose a class-balanced loss function (CB-BCE Loss) to tackle the issue of uneven distribution of defective samples in the Micro LED dataset. To further enhance convergence speed and detection precision, we introduce the SIoU Loss combined with Meta-AconC. Our experimental results, conducted on the Micro LED dataset, demonstrate notable improvements with CM-YOLOv5 over the basic YOLOv5 algorithm. Specifically, CM-YOLOv5 exhibits a 3.8 % increase in mean average precision and a 3.7 % improvement in precision, surpassing current mainstream object detection algorithms, including YOLOR, YOLOX, and YOLOv6, etc., in terms of general evaluation metrics. Finally, upon deploying our proposed algorithm on the edge device NVIDIA Jetson Xavier NX, CM-YOLOv5 demonstrates commendable speed and detection performance in embedded scenarios.