摘要
针对航母甲板面舰载机密集易遮挡,舰载机目标难以检测,且检测效果易受光照条件和目标尺度影响的问题,提出了一种改进的更快的区域卷积神经网络(Faster R-CNN)舰载机目标检测方法。该方法设计了带排斥损失策略的损失函数,并结合多尺度训练,利用实验室条件下采集的图片对深度卷积神经网络进行训练并测试。测试实验显示,相对于原始Faster R-CNN检测模型,改进后的模型对遮挡舰载机目标具有良好的检测效果,召回率提高了7个百分点,精确率提高了6个百分点。实验结果表明,所提的改进方法能够自动全面地提取舰载机目标特征,解决了遮挡舰载机目标的检测问题,检测精度和速度均能够满足实际需要,且在不同的光照条件和目标尺度下适应性强,鲁棒性较高。
The carrier-based aircrafts on the carrier deck are dense and occluded,so that the carrier-based aircraft targets are difficult to detect,and the detection effect is easily affected by the lighting condition and target size.Therefore,an improved Faster R-CNN(Faster Region with Convolutional Neural Network)carrier-based aircraft target detection method was proposed.In this method,a loss function with a repulsion loss strategy was designed,and combined with multi-scale training,pictures collected under laboratory condition were used to train and test the deep convolutional neural network.Test experiments show that compared with the original Faster R-CNN detection model,the improved model has a better detection effect on occluded aircraft targets,the recall increased by 7 percentage points,and the precision increased by 6 percentage points.The experimental results show that the proposed improved method can automatically and comprehensively extract the characteristics of carrier-based aircraft targets,solve the detection problem of occluded carrier-based aircraft targets,has the detection accuracy and speed which can meet the actual needs,and has strong adaptability and high robustness under different lighting conditions and target sizes.
作者
朱兴动
田少兵
黄葵
范加利
王正
陈化成
ZHU Xingdong;TIAN Shaobing;HUANG Kui;FAN Jiali;WANG Zheng;CHENG Huacheng(Coast Guard Academy,Naval Aviation University,Yantai Shandong 264001,China;Department of Ship Surface Aviation Support and Station Management,Naval Aviation University(Qingdao Campus),Qingdao Shandong 266000,China)
出处
《计算机应用》
CSCD
北大核心
2020年第5期1529-1533,共5页
journal of Computer Applications
关键词
舰载机目标检测
排斥损失策略
更快的区域卷积神经网络
多尺度训练
carrier-based aircraft target detection
repulsion loss strategy
Faster Region with Convolutional Neural Network(Faster R-CNN)
multi-scale training
