基于YOLO-IDSTD算法的红外弱小目标检测

针对复杂背景下红外弱小目标难以准确快速检测的问题，提出了一种红外弱小目标轻量化实时检测网络模型YOLO-IDSTD。首先，为提高检测速度，重新设计了特征提取部分的网络结构，并在输入层后使用Focus模块以减少推理时间；其次，为增强检测能力，特征融合部分采用路径聚合网络，添加了改进的感受野增强模块；最后，目标检测部分增加至四尺度检测。在红外弱小目标数据集上进行的对比实验表明，相较于经典轻量化模型YOLOv3-tiny，文中提出的模型召回率提升了7.57%，平均检测精度提高了1.92%，CPU推理速度提升了36.1%，可较好地兼顾精度和速度，计算量与参数量明显减少，模型尺寸压缩至7.27 MB，减少了对硬件平台运算能力的依赖，实现了红外弱小目标准确又快速的检测。

Infrared dim and small target detection based on YOLO-IDSTD algorithm

Aiming at the problem that it is difficult to detect infrared dim and small target accurately and quickly in complex background, a lightweight real-time network model YOLO-IDSTD for infrared dim and small target detection was proposed. Firstly, in order to improve the detection speed, the network structure of the feature extraction part was redesigned, and the Focus module was used to reduce the reasoning time after the input layer. Secondly, in order to enhance the detection ability, the path aggregation network was adopted in the feature fusion part and an improved receptive field block was added. Finally, four-scales detection was increased in the target detection part. Compared with the classical lightweight model YOLOv3-tiny on the infrared dim and small target data set, the recall is increased by 7.57%, the average pricision is increased by 1.92%, and the CPU reasoning speed is increased by 36.1%. The model can balance accuracy and speed, and the amount of calculation and parameters are significantly reduced. The size of the model is compressed to 7.27 MB, which reduces the dependence on the computing power of the hardware platform and realizes the accurate and fast detection of infrared dim and small targets.