小样本红外图像的样本扩增与目标检测算法

深度卷积神经网络模型在很多公开的可见光目标检测数据集上表现优异,但是在红外目标检测领域,目标样本稀缺一直是制约检测识别精度的难题.针对该问题,本文提出了一种小样本红外图像的样本扩增与目标检测算法.采用基于注意力机制的生成对抗网络进行红外样本扩增,生成一系列保留原始可见光图像关键区域的红外连续图像,并且使用空间注意力机制...

Infrared image sample amplification and object detection method with small samples

The deep convolutional neural network model performs well on many public visible-lighted object detectiondatasets, but in the field of infrared object detection, the scarcity of object samples has always been a problem that plaguesthe increase in detection and recognition accuracy. In response to this problem, this paper proposes an algorithm forsample amplification and object detection of infrared images with small samples. The attention-based generative adversarialnetwork was adopted to amplify the infrared samples to generate a series of infrared continuous images retaining the keyareas of the original visible light image, and the spatial attention model and other methods were used to further improvethe recognition accuracy of the YOLOv3 object detection algorithm. The experimental results on the OSU Color-Thermalinfrared-visible light dataset and Grayscale-Thermal dataset show that the infrared data amplification technology of theproposed algorithm effectively improves the accuracy of the deep convolutional neural network for infrared object detection,and the mAP (mean average precision) of the proposed method is 20% higher than the original YOLOv3 algorithm.