The complex heterogeneous background in the nearinfrared images of photovoltaic solar cells makes the detection of internal defects become a very challenging problem.Thus, an object detection framework based on deeplearning residual channel attention Faster RCNN (RCAFaster RCNN) is proposed, which employs convolution layer and pooling layer to extract the image features, and sends them to the novel residual channel attention (RCA) module for complex background feature suppression and defect feature highlighting, then the region proposal network recommends a more accurate proposal containing defects, finally the classification and position network is applied to achieve highprecision defect classification and position estimation.The experimental results show that the defect detection accuracy of RCAFaster RCNN has improved to 8329%, which proves the effectiveness of the proposed method.