Real-Time Tracking Based on Improved YOLOv5 Detection in Orchard Environment for Dragon Fruit

Abstract

Highlights This method has achieved faster detection speed while maintaining accuracy. It is a real-time tracking method that can track dragon fruits in orchard environments in real-time. The introduction of an attention mechanism in the network provides good robustness to changes in lighting and target scale. Abstract. This article addresses the issue of dragon fruit real-time detection in orchard environments and proposes a real-time detection and tracking model for dragon fruit using an improved YOLOv5 object detection algorithm and Deep-sort object tracking algorithm. By applying real-time tracking to dragon fruit harvesting, the tracking algorithm provides timely feedback on the fruit's location, allowing for prompt correction of environmental issues that may affect the accuracy of the harvesting process. This approach enhances the robustness of the target positioning algorithm. First,based on the YOLOv5 object detection algorithm, the Convolutional Block Attention Module and Transformer self-attention mechanism are introduced to construct a YOLOv5s-DFT object detection model that is more suitable for dragon fruit detection. Next, Combining the Deep-sort multi-object tracking algorithm, this article proposes a real-time detection and tracking method for dragon fruit in the orchard environment. The YOLOv5s-DFT model was trained and experimented with using a self-built dataset. The trained model weight is only 19.26% of YOLOv7. The experimental result shows that, while ensuring detection accuracy, YOLOv5s-DFT has a faster detection speed in dragon fruit detection, with an average frame time of 0.01673 s, which is 0.00422 s faster than the original YOLOv5s. When tracking dragon fruit using the Deep-sort tracking algorithm, it can track dragon fruit at a speed of 47.08 frames per second. When utilizing the Deep-sort tracking algorithm to track dragon fruit, it achieves a tracking speed of 47.08 frames per second, enabling real-time acquisition of the fruit's position information. This technology provides technical assistance for the intelligent harvesting of dragon fruit and the intelligent management of dragon fruit orchards. Keywords: Dragon fruit, Improved YOLOv5, Orchard environment, Real-time tracking.