论文标题
使用深度学习的小物体检测
Small Object Detection using Deep Learning
论文作者
论文摘要
如今，无人机之类的无人机被极大地用于各种目的，例如从Ariel图像中捕获和目标检测等。轻松地将这些小型Ariel车辆访问公众可能会造成严重的安全威胁。例如，可以使用无人机在公共场合混合的间谍来监视关键地方。手头的研究提出了一个改进，有效的基于深度学习的自主系统，该系统可以精确地检测和跟踪非常小的无人机。提出的系统由自定义的深度学习模型Tiny Yolov3组成，这是您仅构建并用于检测的非常快的对象检测模型的口味之一。对象检测算法将有效地检测无人机。与以前的Yolo版本相比，所提出的架构显示出明显更好的性能。在资源使用和时间复杂性的术语中观察到了改进。使用召回和精度的指标分别为93％和91％来衡量性能。
Now a days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study in hand proposes an improved and efficient Deep Learning based autonomous system which can detect and track very small drones with great precision. The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You Look Only Once (YOLO) is built and used for detection. The object detection algorithm will efficiently the detect the drones. The proposed architecture has shown significantly better performance as compared to the previous YOLO version. The improvement is observed in the terms of resource usage and time complexity. The performance is measured using the metrics of recall and precision that are 93% and 91% respectively.
