复杂场景下的红外目标检测

Infrared target detection and recognition in complex scene

主流的目标检测网络在高质量RGB图像上的目标检测能力突出,但应用于分辨率低的红外图像上时目标检测性能则有比较明显的下降。为了提高复杂场景下的红外目标检测识别能力,本文采用了以下措施:第一、借鉴领域自适应的方法,采用合适的红外图像预处理手段,使红外图像更接近RGB图像,从而可以应用主流的目标检测网络进一步提高检测精度。第二、采用单阶段目标检测网络YOLOv3作为基础网络,并用GIOU损失函数代替原有的MSE损失函数。经实验验证,该算法在公开红外数据集FLIR上检测的准确率提升明显。第三、针对FLIR数据集存在的目标尺寸跨度大的问题,借鉴空间金字塔思想,加入SPP模块,丰富特征图的表达能力,扩大特征图的感受野。实验表明,所采用的方法可以进一步提高目标检测的精度。

The mainstream target detection network has outstanding target detection capability in high quality RGB images,but for infrared images with poor resolution,the target detection performance decreases significantly.In order to improve the performance of infrared target detection in complex scene,the following measures are adopted in this paper:Firstly,by referring to the field adaption and adopting the appropriate infrared image preprocessing means,the infrared image is closer to the RGB image,so that the mainstream target detection network can further improve the detection accuracy.Secondly,based on the one-stage target detection network YOLOv3,the algorithm replaces the original MSE loss function with the GIOU loss function.It is verified by experiments that the detection accuracy on the open infrared data set the FLIR is significantly improved.Thirdly,in view of the problem of large target size span existing in FLIR dataset,the SPP module is added with reference to the idea of the spatial pyramid to enrich the expression ability of feature map,expand the receptive field of feature map,and further improve the accuracy of target detection.