基于YOLOv5的红外目标检测算法

针对红外图像的特点，提出了一种YOLOv5-IF算法，使用了基于残差机制的特征提取网络，实现了不同特征层之间信息的高效交互，能够得到更丰富的目标语义信息。通过改进YOLOv5的检测方案，增加更大尺度的检测头，有效提升了红外图像中小目标的检测概率。针对计算平台资源有限、算法实时性等问题，设计了Detection Block模块，并由此构建了特征整合网络，该模块不仅能提升算法检测精度，还有效缩减了模型参数量。在FLIR红外自动驾驶数据集上，本文算法的平均准确率（mAP）为74%，参数量仅19.5MB，优于现有的算法。

Infrared Target Detection Based on YOLOv5

According to the characteristics of infrared images, a YOLOv5-IF method is proposed. The feature extraction network based on the residual mechanism is used to realize the efficient interaction of information between different feature layers and obtain richer target semantic information. By improving the detection scheme of YOLOv5 and adding a larger-scale detection head, the detection probability of small and medium targets in infrared images is effectively improved. Aiming at the problems of limited computing platform resources and real-time demand, the Detection Block module is designed, and the feature integration network is constructed. This module can not only improve the detection accuracy of the method, but also effectively reduce the number of model parameters. On FLIR infrared automatic driving data set, the average accuracy of the proposed method is 74 %, and the parameter is only 19.5 MB, which is better than the existing methods.