SDE-DET: A Precision Network for Shatian Pomelo Detection in Complex Orchard Environments

2025-09-24

AI Summary

To address the challenges of multi-scale variation, severe occlusion by foliage, and poor detectability of small targets in complex orchard environments for Shatian pomelo detection, this paper introduces STP-AgriData—the first dedicated agricultural dataset for this task—and proposes SDE-DET, a lightweight and efficient detection model. SDE-DET innovatively integrates Star Blocks to balance high-dimensional feature representation and computational efficiency, incorporates deformable attention to enhance occlusion robustness, and designs a multi-scale efficient attention module to improve small-object detection performance. Evaluated on STP-AgriData, SDE-DET achieves state-of-the-art results: Precision = 0.883, mAP@0.5 = 0.838, and mAP@0.5:0.95 = 0.497—significantly outperforming YOLO-based baselines. This work establishes a high-accuracy, occlusion-robust visual perception foundation for autonomous harvesting robots in agriculture.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Pomelo detection is an essential process for their localization, automated robotic harvesting, and maturity analysis. However, detecting Shatian pomelo in complex orchard environments poses significant challenges, including multi-scale issues, obstructions from trunks and leaves, small object detection, etc. To address these issues, this study constructs a custom dataset STP-AgriData and proposes the SDE-DET model for Shatian pomelo detection. SDE-DET first utilizes the Star Block to effectively acquire high-dimensional information without increasing the computational overhead. Furthermore, the presented model adopts Deformable Attention in its backbone, to enhance its ability to detect pomelos under occluded conditions. Finally, multiple Efficient Multi-Scale Attention mechanisms are integrated into our model to reduce the computational overhead and extract deep visual representations, thereby improving the capacity for small object detection. In the experiment, we compared SDE-DET with the Yolo series and other mainstream detection models in Shatian pomelo detection. The presented SDE-DET model achieved scores of 0.883, 0.771, 0.838, 0.497, and 0.823 in Precision, Recall, mAP@0.5, mAP@0.5:0.95 and F1-score, respectively. SDE-DET has achieved state-of-the-art performance on the STP-AgriData dataset. Experiments indicate that the SDE-DET provides a reliable method for Shatian pomelo detection, laying the foundation for the further development of automatic harvest robots.

Problem

• Detecting Shatian pomelos in complex orchard environments with multiple challenges
• Addressing multi-scale issues and obstructions from trunks and leaves
• Improving small object detection accuracy for automated harvesting

Innovation

• Star Block for high-dimensional information acquisition
• Deformable Attention for occlusion handling
• Efficient Multi-Scale Attention for small objects