基于改进YOLOX的红外目标检测算法

Object detection algorithm of thermal infrared images based on improved YOLOX

针对红外目标图像分辨率低,缺少纹理细节,存在复杂背景干扰导致检测精度低的问题,提出一种基于改进YOLOX的红外目标检测算法。首先,设计了一种有效的空间通道混合注意力模块,将其引入在特征提取主干网络CSP-Darknet53中,以减少网络由于远距离传输造成的精度损失;其次,为了进一步提升红外目标的检测精度,在原本加强特征提取网络PANet的基础上提出一种改进的路径特征融合方法;最后,为了解决红外目标中小物体预测精度低的问题,在YOLOX输出检测头处进行反卷积操作扩大输出特征图。在FLIR红外公开数据集上进行实验,实验结果表明,所提算法识别的平均精度均值(mAP)达91.00%,相比于基准YOLOX网络的平均精度提升了5.04个百分点,对于提升红外目标的检测精度是有效的。

To solve the problem of low resolution of infrared target images, lack of texture details, and low detection accuracy caused by complex background interference, an infrared target detection algorithm based on improved YOLOX is proposed. First, an effective spatial channel mixed attention module is introduced into the feature extraction backbone network CSP-Darknet53 to reduce the accuracy loss of the network due to long-distance transmission;secondly, in order to further improve the detection accuracy of infrared targets, based on the original enhanced feature extraction network PANet, an improved path feature fusion method is proposed;finally, in order to solve the problem of low recognition rate of small objects in infrared targets, a deconvolution operation is performed at the YOLOX output detection-head to expand the output feature map. Experiments are carried out on the FLIR infrared public data set. The experimental results show that the mean Average Precision(mAP) of the proposed algorithm recognition reaches 91.00%, which is 5.04% percentage points higher than that of the benchmark YOLOX network, it is effective to improve the detection accuracy of infrared targets.