基于轻量级YOLOv8n网络的PCB缺陷检测算法

针对PCB缺陷检测无法兼顾检测精度与模型体积的问题，提出一种基于轻量级YOLOv8n网络的PCB缺陷检测算法。首先，删除大目标检测层，新增小目标检测层并调整网络结构，使模型轻量化并提高检测精度。其次，将C2f模块结合GhostConv与DWConv设计出C2f-GhostD模块替换C2f模块，减少模型计算成本。然后，将PConv融入Detect模块中，设计出POne-Detect模块并应用于检测网络，精简网络结构。最后，在颈部网络添加SimAM注意力机制，提高信息捕获能力。实验结果表明，在PCB数据集中，该算法相较于YOLOv8n，参数量下降78.7%，模型体积减小73.7%，mAP0.5提升至98.6%，满足模型硬件部署需求。

PCB defect detection algorithm based on lightweight YOLOv8n network

A lightweight YOLOv8n-based algorithm for PCB defect detection is proposed to address the trade-off between detection accuracy and model size. Firstly, the large target detection layer is deleted, the small target detection layer is added, and the network structure is adjusted to make the model lightweight and improve detection accuracy. Secondly, the C2f module is combined with GhostConv and DWConv to design the C2f-GhostD module to replace the C2f module, reducing the computational cost of the model. Then, PConv is integrated into the Detect module, resulting in the POne-Detect module, which is applied to the detection network to streamline its structure. Finally, the SimAM attention mechanism is added to the neck network to improve information capture ability. The experimental results show that in the PCB dataset, compared with YOLOv8n, the proposed algorithm reduces the number of parameters by 78.7%, reduces the model size by 73.7%, and improves the mAP0.5 to 98.6%, meeting the hardware deployment requirements of the model.