基于YOLOv3的红外行人小目标检测技术研究

针对红外图像中行人小目标检测识别率低、虚警率高的问题,研究了当下效果最好的YOLOv3目标检测算法,在其基础上进行优化,提出了一种满足实时性要求的行人小目标检测算法。基于YOLOv3中分类准确率仍有不足的情况,借鉴SENet中对特征进行权重重标定的思路,将SE block引入YOLOv3中,提升了网络的特征描述能力。通过对自行收集实际复杂场景下的红外图像进行目标检测,试验验证了算法的可行性,实验结果表明本文提出的改进网络拥有更高的准确率和更低的虚警率,同时保持了原有算法的实时性。

Research of Infrared Small Pedestrian Target Detection Based on YOLOv3

To solve the problem of low recognition rate and high false alarm rate in the study of small pedestrian target detection in infrared image,this paper studies YOLOv3,one of the best target detection algorithms,and based on it proposes a small pedestrian detection algorithm that meets real-time requirements.Based on the fact that the classification accuracy is still insufficient in YOLOv3,this article studies the idea of feature reweighting from SENet,and introduces the SE block into YOLOv3,which improves the feature modeling ability of the network.The feasibility of the algorithm is verified by experiments with infrared images collected in actual complex scenes.The experiment results show that the improved network has higher accuracy and lower false alarm rate in small pedestrian detection task,and the algorithm maintains real-time characteristics of the original algorithm.