基于YOLOv3的正下无人机视角挖掘机实时检测方法

针对无人机巡检的智能化要求, 提出一种应对高空巡检场景下的实时挖掘机检测模型. 该模型以YOLOv3为基础, 将骨干网络精简至43层, 通过特征融合策略使检测任务在两个尺度上进行. 此外模型还借鉴了focal loss的思想设计损失函数. 文中实验对象为正下无人机视角的挖掘机目标. 在完成了数据集的搭建工作后, 根据正下无人机视角的目标特性进行训练, 使模型达到最优解. 最终经实验验证, 在相同输入尺寸的情况下, 本文所提出的检测模型比YOLOv3准确率更高、鲁棒性更好, 且帧数可提升10帧●s^-1.

Real-time excavator detection under direct UAV view based on improved YOLOv3 method

A real-time excavator detection method for high-altitude patrol scenarios is proposed to meet the requirements of Unmanned Aerial Vehicle (UAV) intelligent inspection. The model is based on YOLOv3 and adopts a 43-layer backbone network. The object can be detected on two scales through feature fusion strategy. Besides, it also defines the loss function with the idea of focal loss. The experimental object in this article is an excavator under the direct UAV view. Upon the completion of the construction of the dataset, the optimal solution of the model is obtained by training the model with characteristics of the object under direct UAV view. The experimental results show that the proposed model is more accurate and robust than YOLOv3 for the same input size, and has increased by 10 frame per second in terms of frame number.