红外热成像中低分辨率行人小目标检测方法

红外热成像图像的目标检测中，针对低分辨率小目标检测效果差、复杂尺度目标检测率低等问题，提出一种基于改进YOLOv5的红外低分辨率目标检测算法。选用LLVIP红外数据集，通过引入不同注意力机制来对比检测效果。选用效果最佳的注意力机制，改进目标检测网络的损失函数提高对小目标的检测率。利用TiX650热成像仪采集小目标图像样本对原数据集进行优化采样和增广，分别使用改进前后的YOLOv5网络进行训练。从模型训练结果和目标检测结果评估模型的性能提升，实验结果表明:相较于原始训练模型，改进后YOLOv5的训练模型，在红外成像的同一场景中对低分辨率小目标的检测精度上有明显提升，且漏检率低。

Infrared Thermal Imaging Low-Resolution and Small Pedestrian Target Detection Method

In the target recognition of infrared thermal imaging images, a detection algorithm based on improved YOLOv5 for infrared low-resolution targets was proposed to address the poor detection of low-resolution small targets and low detection rate of complex-scale targets. The LLVIP infrared dataset was selected and the detection effect was compared by introducing different attention mechanisms. The attention mechanism with the best effect was selected to improve the loss function of the target detection network and improve the detection rate of small targets. A TiX650 thermal imager was utilized to acquire small target image samples for optimal sampling and broadening of the original dataset, and the YOLOv5 network was trained using the improved before and after, respectively. The performance improvement of the model was evaluated from the model-training and target detection results, and the experimental results demonstrate that compared with the original training model, the improved YOLOv5 training model has a significant improvement in the detection accuracy of low-resolution small targets in the same scene of infrared imaging and exhibits a low miss detection rate.