摘要
随着无人机广泛运用给人们带来便利的同时,也带来了一些新问题,如无人机非法入侵、碰撞行人等引发安全隐患,因此需要建立一套对指定区域无人机目标进行实时、准确地识别和监控系统。针对图像中无人机目标快速检测和识别问题,提出了两种基于SSD(Single Shot MultiBox Detector)算法的实时无人机识别方法。一种方法是基于SSD获取视频流中的无人机位置,然后利用ResNet网络提取无人机的深度特征,得到1000维特征向量,最后采用KNN(K-Nearest Neighbor)算法对提取的特征进行分类,得到最终的无人机识别结果;在收集的无人机测试集中识别准确率达到了79%。另一种方法是直接将SSD检测到的无人机目标图像送入到AlexNet网络中进行Fine-tuning(微调),在无人机测试集中识别准确率达到了83.75%。实验结果表明,两种方法都能实现实时无人机识别,且准确性方面第二种方法优于第一种方法。同时采用Storm框架,保证高吞吐量地处理数据。
The wide use of the Unmanned Aerial Vehicle(UAV)brings convenience to people,it also causes some new questions. For example,the UAV fly into No-fly zone,and people collision accident which results in safety problem. Therefore,a real-time and accurately recognition,monitor and control system of the UAV target inspecified areas are needed. To settle the problem of fast detecting and recognizing the UAV target in the images,two real-time UAV recognition methods based on SSD algorithm are proposed. In one method,Firstly,the location of UVA from a video stream is grabbed using SSD. Secondly,by utilizing ResNet UVA deep feature is extracted,and then 1000 dimension vectors are gotten. At last,KNN algorithm is used to classify the extracted feature vectors,and then the UVA recognition result is gotten. The recognition accuracy has reached 79% in the collected UVA testing set. In another method,the detected UVA by SSD is sent into AlexNet to do fine-tuning,whose accuracy has reached 83.75% in the UVA testing set. Experiments show that the two method both can accomplish real-time UAV recognition, and the second method is better than the first method on the aspect of accuracy. By incorporating the Storm framework,it ensures that data is processed with high throughput.
作者
李秋珍
熊饶饶
王汝鹏
祁迪
LI Qiuzhen;XIONG Raorao;WANG Rupeng;QI Di(Wuhan Digital Engineering Institute,Wuhan 430074;School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074)
出处
《舰船电子工程》
2019年第5期30-35,共6页
Ship Electronic Engineering
基金
中国船舶重工集团公司十三五联合基金项目(编号:6141B04050101-10)资助
