一种基于透视变换数据增广的斜视目标鲁棒检测方法

目标检测通过运用卷积神经网络技术，使得在识别的精度上取得非常大的进步。通用的目标检测已经取得较好的检测效果，但是针对工业生产中存在样本量较少的斜视目标问题，算法检测效果较差。主要的原因是训练样本非常稀少，造成基于深度神经网络的检测模型训练发生偏移，对整体的检测精度造成影响。本文提出一种基于透视变换数据增广的斜视目标鲁棒检测方法，通过透视变换模拟斜视目标出现的场景，解决斜视目标样本量较少的问题，同时能够显著增加用于训练的斜视目标样本量，提高斜视目标的识别精度。实验结果表明本文提出的方法对检测精度的提高效果明显。

A Squint Object Robust Detection Method Based on #br# Perspective Transformation Data Augmentation

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.