ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds

Abstract

In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

“…The backbone is basically the same as that of YOLOv5 and consists of a series of convolutional and deconvolutional layers to extract features. It also incorporates residual connections and bottleneck structures to reduce network size and improve performance [29]. This part uses the C2f module as the basic building block that replaces the C3 module in YOLOv5.…”

“…detection results, while the classification head uses global average pooling to classify each feature map. It also adopts an anchor-free strategy, abandoning the anchor boxes used in YOLOv7, which reduces the number of box predictions and improves the speed of Non-Maximum Suppression (NMS)[29]. For loss computation, YOLOv8 uses the Task Aligned Assigner positive sample assignment strategy.…”

The transmission line foreign body detection algorithm based on weighted spatial attention

“…The YOLOv8 detection algorithm is a lightweight anchor-free model that directly predicts the center of extraneous materials within power conduits instead of the offset of the known anchor frame (Talaat and ZainEldin, 2023 ; Pan et al, 2024 ). The algorithm can quickly locate the foreign objects in transmission lines to be detected during detection.…”