Zhang Qianqian, Zhou Li, An Junshe
National Space Science Center, Chinese Academy of Sciences, Beijing 101499, China.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 May 12;24(10):3075. doi: 10.3390/s24103075.
Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model's real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.
无人机（UAV）红外检测存在目标弱小、背景复杂以及实时检测性能差等问题。一般的目标检测算法难以达到高检测率、低漏检率和高实时性的要求。为了解决这些问题，本文提出了一种基于Picodet的改进型小目标检测方法。首先，为了解决实时性能差的问题，引入了改进的轻量级LCNet网络作为特征提取的主干网络。其次，为了解决因目标弱小导致的高误检率和漏检率问题，添加了挤压激励模块并改进了特征金字塔结构。在HIT-UAV公共数据集上获得的实验结果表明，改进后的检测模型实时帧率提高了31帧/秒，平均精度（MAP）提高了7%，证明了该方法用于无人机红外小目标检测的有效性。
