Detection and Localization of Tea Bud Based on Improved YOLOv5s and 3D Point Cloud Processing

Abstract

Currently, the detection and localization of tea buds within the unstructured tea plantation environment are greatly challenged due to their small size, significant morphological and growth height variations, and dense spatial distribution. To solve this problem, this study applies an enhanced version of the YOLOv5 algorithm for tea bud detection in a wide field of view. Also, small-size tea bud localization based on 3D point cloud technology is used to facilitate the detection of tea buds and the identification of picking points for a renowned tea-picking robot. To enhance the YOLOv5 network, the Efficient Channel Attention Network (ECANet) module and Bi-directional Feature Pyramid Network (BiFPN) are incorporated. After acquiring the 3D point cloud for the region of interest in the detection results, the 3D point cloud of the tea bud is extracted using the DBSCAN clustering algorithm to determine the 3D coordinates of the tea bud picking points. Principal component analysis is then utilized to fit the minimum outer cuboid to the 3D point cloud of tea buds, thereby solving for the 3D coordinates of the picking points. To evaluate the effectiveness of the proposed algorithm, an experiment is conducted using a collected tea image test set, resulting in a detection precision of 94.4% and a recall rate of 90.38%. Additionally, a field experiment is conducted in a tea experimental field to assess localization accuracy, with mean absolute errors of 3.159 mm, 6.918 mm, and 7.185 mm observed in the x, y, and z directions, respectively. The average time consumed for detection and localization is 0.129 s, which fulfills the requirements of well-known tea plucking robots in outdoor tea gardens for quick identification and exact placement of small-sized tea shoots with a wide field of view.