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arXiv · 2510.09948

A Multi-Strategy Framework for Enhancing Shatian Pomelo Detection in Real-World Orchards

Pan Wang·Yihao Hu·Xiaodong Bai·Jingchu Yang·Leyi Zhou·Aiping Yang·Xiangxiang Li·Meiping Ding·Jianguo Yao
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Abstract

Shatian pomelo detection in orchards is essential for yield estimation and lean production, but models tuned to ideal datasets often degrade in practice due to device-dependent tone shifts, illumination changes, large scale variation, and frequent occlusion. We introduce STP-AgriData, a multi-scenario dataset combining real-orchard imagery with curated web images, and apply contrast/brightness augmentations to emulate unstable lighting. To better address scale and occlusion, we propose REAS-Det, featuring Global-Selective Visibility Convolution (GSV-Conv) that expands the visible feature space under global semantic guidance while retaining efficient spatial aggregation, plus C3RFEM, MultiSEAM, and Soft-NMS for refined separation and localization. On STP-AgriData, REAS-Det achieves 86.5% precision, 77.2% recall, 84.3% mAP@0.50, and 53.6% mAP@0.50:0.95, outperforming recent detectors and improving robustness in real orchard environments. The source code is available at: https://github.com/Genk641/REAS-Det.

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Pan Wang, Yihao Hu, Xiaodong Bai, Jingchu Yang, Leyi Zhou, Aiping Yang, Xiangxiang Li, Meiping Ding, Jianguo Yao. 2025-10-11. A Multi-Strategy Framework for Enhancing Shatian Pomelo Detection in Real-World Orchards. https://arxiv.org/abs/2510.09948

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