关键词: 目标检测, 透视变换, 数据增强, 样本不平衡

Abstract: Object detection makes great progress in the accuracy of recognition by using convolutional neural network technology. The general object detection has achieved better detection results, but the algorithm detection effect is poor for the strabismus object problem with less sample size in industrial production. The main reason is that the training samples are very rare, resulting in shift of the detection model training based on deep neural network, which affects the overall detection accuracy. This paper proposes a squint object robust detection method based on perspective transformation data augmentation. It can solve the problem of less strabismus object sample size by perspective transformation to simulate the scene of strabismus object, increase the squint object sample size for training, and improve the accuracy of recognition of squint objects. Experiments show that the proposed method has obvious improvement effect on detection accuracy.

Key words: target detection, perspective transformation, data enhancement, sample imbalance