针对红外图像检测与分割任务中颜色信息缺失,特征细节模糊并带有噪声,当目标数量较多时传统方法提取过程速度较慢等问题,提出一种用于远红外图像的优化YOLO检测与分割网络模型。提出的两个优化点分别为:综合分析实验使用的两种远红外数据集后使用K-means++聚类算法寻找多尺度预测标记锚点框尺寸;使用局部检测位置自适应阈值分割方法对检测目标进行像素级实例分割。本文优化算法在FLIR公开数据集与本文数据集中的检测速度分别为29 frame/s与28 frame/s,保证了实时输出的要求;行人检测准确率分别达到75.3%与77.6%,分割结果平均交并比达到70%~90%。实验结果表明,本文算法具有良好的稳健性和普适性,在远红外图像中可快速有效地检测行人并生成实例掩模。

In infrared image detection and segmentation tasks, the color information is lost, the features are fuzzy with noise, the target number is large, and the traditional extraction method is slow. Therefore, we propose an optimized YOLO detection and segmentation network model for far-infrared images. The two proposed optimization points are as follows. We use the K-means++ clustering algorithm to determine the multi-scale prediction anchor size after the analysis of two far-infrared databases. We also perform pixel-level instance segmentation of detection targets using localized adaptive threshold segmentation. The experimental results show that the proposed algorithm performs pedestrian detection at detection speeds of 29 frame/s and 28 frame/s on the FLIR dataset and the dataset used in this paper, respectively, ensuring the requirement of real-time output. The pedestrian detection accuracies in these datasets reach 75.3% and 77.6%. Moreover, the average intersection over the union of the segmentation results is 70%--90%. In summary, the algorithm performs well with respect to robustness and universality. The algorithm provides a valuable reference method for pedestrian detection and segmentation in far-infrared fields.