摘要
传统方法无法获得理想的红外弱小车辆目标检测结果,导致检测误差大,无法满足实际应用要求,为了解决传统红外弱小车辆目标检测方法存在的局限性,及时检测红外图像中的弱小车辆,提高车辆检测精度,设计了基于卷积神经网络的红外弱小车辆目标检测方法。首先对弱小车辆目标检测需要的红外图像进行采集,并对红外图像噪声进行处理,消除噪声对弱小车辆目标检测的干扰,然后采用卷积神经网络建立弱小车辆目标检测模型,最后通过具体仿真实验测试弱小车辆目标检测方法的性能。结果表明,该方法的弱小车辆目标检测精度超过了90%,大幅度减少了弱小车辆目标的误检率,同时弱小车辆目标检测时间控制在5 s内,可以满足弱小车辆目标检测的实时性要求,具有较高的实际应用价值。
Traditional methods are unable to obtain ideal infrared weak vehicle target detection results,resulting in large detection errors that cannot meet practical application requirements.In order to address the limitations of traditional infrared weak vehicle target detection methods,timely detect weak vehicles in infrared images,and improve vehicle detection accuracy,a convolutional neural network-based infrared weak vehicle target detection method was designed.Firstly,the infrared images required for weak and small vehicle target detection are collected,and the noise in the infrared images is processed to eliminate the interference of noise on weak and small vehicle target detection.Then,convolutional neural network is used to establish a weak and small vehicle target detection model.Finally,the performance of the weak and small vehicle target detection method in this paper is tested through specific simulation experiments.The results show that the detection accuracy of weak and small vehicle targets using this method exceeds 90%,significantly reducing the false detection rate of weak and small vehicle targets.At the same time,the detection time of weak and small vehicle targets is controlled within 5 seconds,which can meet the real-time requirements of weak and small vehicle target detection and has high practical application value.
作者
金宝根
吕庆梅
JIN Baogen;LYU Qingmei(Shaoxing University,Shaoxing Zhejiang 312000,China)
出处
《激光杂志》
CAS
北大核心
2024年第5期241-245,共5页
Laser Journal
基金
浙江省自然科学基金(No.LY20F050011)。
关键词
红外图像
卷积神经网络
弱小目标
车辆检测
特征向量
噪声抑制
infrared image
convolutional neural network
weak targets
vehicle inspection
feature vector
noise suppression
