Algorithm for surface flow velocity measurement in trunk canal based on improved YOLOv8 and DeepSORT

Abstract

The velocity measurement of trunk canal and river plays an important role in agriculture and forestry irrigation scheduling, water resources management and flood prediction. Particle flow measurement technology can realize non-contact and high-precision flow measurement, but in practical application, the particle size is small, the shape is different and the dynamic change brings great challenges to the application of this method. To solve these problems, this paper proposed the surface velocity measurement method of trunk canal based on improved YOLOv8(You Only Look Once Version 8) and DeepSORT(Deep Simple Online and Realtime Tracking), and introduced tiny detection layer and channel attention mechanism to improve YOLOv8's detection capability of small targets. In DeepSORT, IBN-Net(Intent-Based Networking-Network) network structure and GIoU(Generalized Intersection over Union) matching are introduced to solve the problem of discontinuity or loss of target tracking in complex cases, which improves the accuracy and robustness of target tracking. The experimental results show that the improved YOLOv8 improves AP(Average Precision) and mAP(mean Average Precision) by nearly 5% and 0.2% respectively. The performance of the improved DeepSORT has been improved across the board, especially IDP and MOTA, which have improved by 25.2% and 5.6% respectively. The algorithm also has good accuracy in actual velocity measurement.