An object detection method for bayberry trees based on an improved YOLO algorithm

Abstract

To quickly detect and count the number of bayberry trees, this paper improves the YOLO-v4 model and proposes an optimal YOLO-v4 method for detecting bayberry trees based on UAV images. We used the Leaky_ReLU activation function to accelerate the model extraction speed and used the DIoU NMS to retain the most accurate prediction boxes. In order to increase the recall rate of the object detection and construct the optimal YOLO-v4 model, the K-Means clustering method was embedded into DIoU NMS. We trained the model using UAV images of bayberry trees, it was determined that the optimal YOLO-v4 model threshold was 0.25, which had the best extraction effect. The optimal YOLO-v4 model had a detection accuracy of up to 97.78% and a recall rate of up to 98.16% on the dataset. The optimal YOLO-v4 model was compared with YOLO-v4, YOLO-v4 tiny, the YOLO-v3 model, and the Faster R-CNN model. With guaranteed accuracy, the recall rate was higher, up to 97.45%, and the detection of bayberry trees was better in different contexts. The result shows that the optimal YOLO-v4 model can accurately achieve the rapid detection and statistics of the number of bayberry trees in large-area orchards.

Keywords

• object detection
• yolo-v4
• deep learning
• bayberry trees
• plant number statistics