High-precision object detection network for automate pear picking

To address the urgent need for agricultural intelligence in the face of increasing agricultural output and a shortage of personnel, this paper proposes a high precision object detection network for automated pear picking tasks. The current object detection method using deep learning does not fully c...

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Bibliographic Details
Published in:Scientific reports 2024-06, Vol.14 (1), p.14965-20, Article 14965
Main Authors: Zhao, Peirui, Zhou, Wenhua, Na, Li
Format: Article
Language:English
Subjects:
704/172
704/445
Accuracy
Agricultural intelligence
Agricultural production
Algorithms
Automation
Cameras
Deep learning
Depth perception
Efficiency
Fruits
Humanities and Social Sciences
Information processing
multidisciplinary
Neural networks
Non-maximum suppression
Object detection
Production costs
Productivity
R&D
Research & development
Robots
Science
Science (multidisciplinary)
Sensors
Telematics
YOLOv8
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Summary:To address the urgent need for agricultural intelligence in the face of increasing agricultural output and a shortage of personnel, this paper proposes a high precision object detection network for automated pear picking tasks. The current object detection method using deep learning does not fully consider the redundant background information of the pear detection scene and the mutual occlusion characteristics of multiple pears, so that the detection accuracy is low and cannot meet the needs of complex automated pear picking detection tasks. The proposed, High-level deformation-perception Network with multi-object search NMS(HDMNet), is based on YOLOv8 and utilizes a high-level Semantic focused attention mechanism module to eliminate irrelevant background information and a deformation-perception feature pyramid network to improve accuracy of long-distance and small scale fruit. A multi-object search non-maximum suppression is also proposed to choose the anchor frame in a combined search method suitable for multiple pears. The experimental results show that the HDMNet parameter amount is as low as 12.9 M, the GFLOPs is 41.1, the mAP is 75.7%, the mAP50 reaches 93.6%, the mAP75 reaches 70.2%, and the FPS reaches 73.0. Compared with other SOTA object detection methods, it has the transcend of real-time detection, low parameter amount, low calculation amount, high precision, and accurate positioning.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-65750-6