Abstract:According to the specific image feature that the bird echo shows obvious ring shape in the weather radar reflectivity product, this article proposes an improved algorithm based on a lightweight convolutional neural network You Only Look Once Version5 （YOLOv5） and multi-object tracking based on deep learning based simple online and realtime tracking （DeepSort）. The training and test datasets are constructed based on radar volume scanning echo intensity. Data obtained from the Yingkou Weather Radar from 2020 to 2023. The bird echoes are tracked, respectively. Firstly, Shuffle Attention （SA）, a lightweight attention mechanism, is introduced into YOLOv5 algorithm to improve the accuracy and effectiveness of the overall model checking. Secondly, in DeepSort algorithm, the original cross-merge-ratio intersection over union （IOU） matching mechanism is replaced by an improved loss function of object detection, distance-intersection over union （DIOU） matching mechanism. DIOU introduces the distance between the center points of the boundary box on the basis of calculating the overlap degree of the boundary box, so as to provide more accurate positioning. The number of identification （ID） error matching and ID switching caused by partial occlusion overlap is reduced. The test results show that the optimized YOLOv5 algorithm improves the accuracy by 2.6 percentage point, the recall rate by 1 percentage point, and the average accuracy of threshold values greater than 0.5 by 1.2 percentage point. The improved DeepSort algorithm reduces the number of ID switches by 2 times, and multi target tracking accuracy multi-object tracking accuracy （MOTA） increases by 4.5 percentage point, thus improves lightweight of the initial model. Generally, the overall checking performance is significantly improved, and may meet the actual demand for bird echo recognition and tracking.