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.