基于Transformer的FPC缺陷检测模型研究
Research on FPC Defect Detection Model Based on Transformer
郭航 1田辉 1戴佐俊1
作者信息
- 1. 北京邮电大学信息与通信工程学院，北京，100876
- 折叠
摘要
随着FPC在各类电子产品中的广泛应用，对FPC的工业检测需求也逐渐增加。针对FPC缺陷检测主要依赖人工目视检测和电性测试造成的效率低且漏检率高，以及传统计算机视觉模型难以从FPC复杂缺陷中提取有效信息等问题，本文设计了基于Transformer的FPC缺陷检测模型。FPC缺陷具有缺陷与背景情况复杂多样、图像之间相似度较高、缺陷图像尺度变化大的特点。因此本文将Transformer模型与Faster R-CNN模型进行融合，使用多头非局部Transformer模块（MNT）提升对缺陷特征的提取能力，并设计了通道选择融合特征金字塔模块（CSF-PAFPN）增强模型对多尺度缺陷特征的检测性能。与其他主流模型对比MAP提升均超过0.5%，相比于Faster R-CNN模型MAP提升1.77%。并通过消融实验验证了各模块的有效性。实验结果表明模型在精度、召回率和泛化能力上，能够满足实际生产需求。
Abstract
With the wide application of FPC in various electronic products, the demand for industrial inspection of FPC has gradually increased. In response to the low efficiency and high missed detection rate caused by the main reliance on manual visual inspection and electrical testing for FPC defect detection, as well as the difficulty of traditional computer vision models in extracting effective information from complex FPC defects, this paper designs an FPC defect detection model based on Transformer. FPC defects have the characteristics of complex and diverse defect and background conditions, high similarity between images, and large changes in defect image scale. Therefore, this paper integrates the Transformer model with the Faster R-CNN model, uses the multi-head non-local Transformer module (MNT) to enhance the ability to extract defect features, and designs the channel selection fusion feature pyramid module (CSF-PAFPN) to enhance the model\'s detection performance for multi-scale defect features. Compared with other mainstream models, the MAP improvement is more than 0.5%, and compared with the Faster R-CNN model, the MAP improvement is 1.77%. The effectiveness of each module is verified through ablation experiments. The experimental results show that the model can meet the actual production requirements in terms of accuracy, recall rate, and generalization ability.关键词
计算机视觉/缺陷检测/目标检测/深度学习Key words
Computer vision/defect detection/object detection/deep learning引用本文复制引用
郭航,田辉,戴佐俊.基于Transformer的FPC缺陷检测模型研究[EB/OL].(2025-06-05)[2026-03-11].http://www.paper.edu.cn/releasepaper/content/202506-16.学科分类
计算技术、计算机技术
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