An improved target detection method based on YOLOv5 in natural orchard environments

• Published in: Computers and electronics in agriculture Vol. 219; p. 108780
• Main Authors: Zhang, Jiachuang, Tian, Mimi, Yang, Zengrong, Li, Junhui, Zhao, Longlian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-04-2024
• Subjects: Coordinate Attention; Deep learning; Target detection; YOLOv5; Deep learning; Coordinate Attention; YOLOv5; Target detection
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.108780

•An efficient method is proposed for orchard tree trunk detection in natural scenes.•A multi-scenes orchard tree trunk dataset is established for experiments.•The YOLOv5 model structure is improved to enhance detection accuracy and speed.•Proposed method is robust against complex orchard environment.
The recognition and localization of fruit tree trunks in orchard are important for orchard operation robots, which are the bases for automatic navigation, fruit tree spraying and fertilization etc. A method was proposed based on machine vision to detect target objects such as fruit tree trunks, person and supporters in orchard by improving the YOLOv5 deep learning algorithm in this paper, which is applicable to the recognition tasks in natural orchard environments. Firstly, 1354 images of the natural orchard collected by camera were image enhanced, weather effects such as rain, snow, bright light, shadow and fog were added to expand the dataset and to increase the robustness of the model. Secondly, the original YOLOv5 model was improved by replacing the Bottleneck network in the C3 module with the lightweight GhostNet V2 to reduce the network parameters, and changing the box loss function CIoU to SIoU in the loss function to make the regression of the detection box more accurate, and coordinate attention mechanism (CA) was added to the network to reduce the interference of useless background information in images. Before training, pre-anchor boxes were generated by using IoU-based K-means clustering, after that the dataset was fed into the improved YOLOv5 for training, and the trained model was used to detect the trunks. Finally, weighted boxes fusion (WBF) was used instead of the non-maximum suppression (NMS) in this paper for the output of the detection boxes. Then the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for trunk clustering. The improved target detection method was trained and validated on the experimental dataset. The model size is reduced by 43.6 %, model parameters are reduced by 46.9 %, and the mAP reaches 97.1 %, with an average detection speed of 198.2 ms per image. Compared with the original YOLOv5, the model is more lightweight, the detection accuracy and speed are improved. The improved YOLOv5 is also better than YOLOv3, NanoDet and SSD in terms of combined accuracy and speed, and has similar performance to YOLO_MobileNet in orchard dataset. The experimental results show that the improved YOLOv5 target detection model proposed in this paper is lightweight while still having better detection accuracy and detection speed in complex environments, and the model is small enough to be deployed to mobile or low-performance terminals for target detection in natural orchard environments.