摘要
针对工业环境中噪声干扰导致的印刷电路板缺陷检测困难的问题,提出基于多注意力Faster RCNN的印刷电路板缺陷检测方法,分别于特征提取以及特征融合部分引入注意力机制以获得具有抗干扰能力的特征表示,提升检测效果。首先使用分离注意力网络提取缺陷特征,使网络自动关注到缺陷特征,以降低噪声干扰的影响;其次,使用平衡特征金字塔融合不同分辨率特征,利用非局部注意力机制对融合特征进行全局感受野内不同区域特征的加权,增强其缺陷表征能力并进一步抑制噪声干扰;最后,依据所获得的特征表示,利用区域建议网络生成缺陷候选框并利用全连接层对其进行位置以及类别的确定以得到检测结果。在不同程度噪声干扰下的印刷电路板缺陷数据集上进行实验验证,平均检测精度达到92.4%,证明了所提方法的有效性和可行性。
To address the problem of PCB defect detection caused by noise interference in industrial environment,a PCB defect detection method based on the multi-attention Faster RCNN is proposed.The attention mechanism is introduced into the feature extraction and feature fusion parts to obtain feature representations with anti-interference ability.First,the defective features are extracted by using a split-attention network that automatically focuses on the defective features to reduce the effect of noise interference.Secondly,a balanced feature pyramid is used to fuse different resolution features,and a non-local attention mechanism is utilized to weight the fused features to different regions within the global perceptual field to enhance their defect characterization and further suppress noise interference.Finally,based on the obtained feature representation,the regional proposal network is used to generate defect candidate box.The fully connected layer is utilized to determine defects′position and category to obtain the detection results.Experiments are implemented on the printed circuit board defect data sets under different degrees of noise interference.The average detection accuracy reaches 92.4%,which proves the effectiveness and feasibility of the proposed method.
作者
陈仁祥
詹赞
胡小林
徐向阳
蔡东吟
Chen Renxiang;Zhan Zan;Hu Xiaolin;Xu Xiangyang;Cai Dongyin(Chongqing Engineering Laboratory for Transportation Engineering Application Robot,Chongqing Jiaotong University,Chongqing 400074,China;Chongqing Innovation Center of Industrial Big-Data Co.,Ltd.,Chongqing 400056,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第12期167-174,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51975079)
国家重点研发项目(2018YFB1306601)
重庆市教委科学技术研究项目(KJQN201900721)
重庆市研究生导师团队项目(JDDSTD2018006)资助。
关键词
印刷电路板
缺陷检测
噪声干扰
多注意力
printed circuit board
defect detection
noise interference
multi-attention
