基于深度学习的无人机视觉目标检测与跟踪

针对目标检测中小目标物体漏检率及误检率高等问题，提出了一种基于Yolov3-Tiny算法的改进模型。改进k-means聚类方法，增加3×3和1×1的卷积池化层，将第9层卷积输出上采样，并与第8层卷积得到的特征图进行连接，得到新的输出:52×52卷积层，形成新的特征金字塔。基于卡尔曼滤波算法实现目标跟踪，提出融合跟踪算法的检测网络，使用匈牙利匹配算法对检测边缘框与跟踪边缘框进行最优匹配，利用跟踪结果修正检测结果，提高了检测速度，同时提升了检测能力。在ROS、Gazebo和自动驾驶仪软件PX4的综合仿真环境下对所提算法进行了对比试验。试验结果表明:改进算法平均检测速度降低了15.6%，mAP提高了6.5%。融合跟踪算法后的网络平均检测速度提高了34.2%，mAP提高了8.6%。融合跟踪算法后的网络能够满足系统实时性和准确性的要求。 

Deep learning based UAV vision object detection and tracking

An improved model based on the Yolov3-Tiny algorithm is proposed for object detection with high miss and false detection rates of small target objects. The k-means clustering method is improved by adding 3×3 and 1×1 convolutional pooling layers, upsampling the output of the 9th convolutional layer, and connecting it with the feature map obtained from the 8th convolutional layer to obtain a new output: 52×52 convolutional layers, forming a new feature pyramid. The object tracking is implemented based on Kalman filtering algorithm. And the detection network with fusion tracking algorithm is proposed. The Hungarian algorithm is used to optimally match the detection edge frame with the tracking edge frame, and the tracking result is used to correct the detection result. The detection speed is improved and the detection capability is enhanced at the same time. The proposed algorithm is tested in a comprehensive simulation environment of ROS, Gazebo and autopilot software PX4 for comparison. The test results show that the improved algorithm reduces the average detection speed by 15.6% and increases the mAP by 6.5%. The fusion tracking algorithm improves the average detection speed of the network by 34.2% and the mAP by 8.6%. The network after the implementation of fusion tracking algorithm can meet the requirements of system real-time property and accuracy.