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Using deep learning technology to detect individual bamboo trees in UAV images is a valuable research field. Similar to other object detection tasks, in bamboo detection scenario, data augmentation can greatly increase the number and appearance diversity of bamboo samples, making the trained model has good generalization ability and performs better when predicting bamboo trees. Since different data augmentation operations have different effects on improving the performance of the model, just treating them equally and randomly selecting and combining some of them when training bamboo detection model will not achieve the best result. In this study, we collected 50 km2 of UAV forest aerial images and labeled more than 10,000 bamboo trees as the dataset. Then we analyzed a variety of data augmentation operations suitable for individual bamboo detection scenario and added them into candidate operations. On this basis, we proposed a novel data augmentation operations combining strategy, assigning different weights to different operations and dividing them into different categories, according to their degree of improvement of bamboo detection results. The experimental results shown that compared with the baseline YOLOv5x model without any data augmentation, all selected candidate data augmentation operations could improve the detection result to a certain extent. The proposed operations combining strategy achieved an impressive performance, reaching 91.1%, 85.1% and 79.1% in recall, precision and mAP respectively. Compared with commonly used RandAugment combining strategy, in our strategy, recall was increased by 4.47%, precision was increased by 1.55%, mAP was increased by 6.89%. In order to further verify the generalization ability of the model, we applied strategies to new bamboo forest, the results showed our combination strategy also had advantages in all recall, accuracy and mAP.
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