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.

中文翻译：

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基于自注意力机制的神经网络的 Micro LED 缺陷检测

我们提出了一种利用 YOLO 检测器来精确定位缺陷芯片并识别多尺度多目标图像中的缺陷类型的方法。为了解决优化训练成本和增强模型泛化的挑战，我们引入了专为芯片检测而设计的端到端深度神经网络 CM-YOLOv5。我们将一个新颖的瓶颈层 MA-CSP 与多头自注意力机制 (MHSA) 结合起来。此外，我们提出了类平衡损失函数（CB-BCE Loss）来解决 Micro LED 数据集中缺陷样本分布不均匀的问题。为了进一步提高收敛速度和检测精度，我们引入了与 Meta-AconC 相结合的 SIoU Loss。我们在 Micro LED 数据集上进行的实验结果表明，CM-YOLOv5 相对于基本 YOLOv5 算法有显着改进。具体来说，CM-YOLOv5 的平均精度提升了 3.8%，精度提升了 3.7%，在一般评估指标上超越了当前主流目标检测算法，包括 YOLOR、YOLOX 和 YOLOv6 等。最后，在边缘设备 NVIDIA Jetson Xavier NX 上部署我们提出的算法后，CM-YOLOv5 在嵌入式场景中表现出了值得称赞的速度和检测性能。