A deep learning model for detecting PCB defects based on arbitrary-oriented object detection was proposed， which could simplify the whole process by avoiding image registration and image pre-processing. This model had two new components， including a Rotation Region Proposal Network （RRPN） and a Rotation Region-of-Interest （RRoI） pooling layer. Experiment results validated the effectiveness of the proposed model by achieving 97.2% mAP on perturbated DeepPCB dataset.