基于深度学习的电路面板元件检测研究

CIRCUIT PANRL COMPOMENT DETECTION BASED ON DEEP LEARNING

对电路面板的元器件识别,可大量减少人工成本和时间,避免因人为主观因素而产生不合格样品。根据应用场景要求,提出ResNet50融合EfficientNet的骨架网络方法。该方法增强了物体检测的语义信息,有效解决多尺度检测问题,对各类尺度检测问题有着更好的识别效果。通过骨架网络的特征提取后,加载到双向FPN(Bi-Feature Pyramid networks)层再进行更深层次的特征提取,将高语义特征和浅层的定位特征进行融合。实验结果表明,使用该构建的融合模型,具有精确率高、检测图片的帧率高、鲁棒性好的优点,具有良好的泛化能力。

The component identification of circuit panel can greatly reduce labor cost and time,and avoid unqualified samples due to human subjective factors.According to the requirement of application scenario,this paper proposes a skeleton network method of ResNet50 combined with EfficienctNet.This method enhanced the speech information of object detection,effectively solved the problem of multi-scale detection,and had better recognition effect for various scale detection problems.After the feature extraction of skeleton network,it was loaded into the Bi-feature pyramid networks(Bi-FPN)layer,and further feature extraction was carried out to fuse the high semantic features and the shallow location features.The experimental results show that the proposed fusion model has the advantages of high precision,high frame rate,robustness and good generalization ability.