基于深度学习的无人机视觉目标检测与跟踪

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.