对比36个实时检测模型，提升蓝莓识别精度与效率。
A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management
- 对比YOLOv8-v12与RT-DETRv1-v2共36种模型性能。
- 最优模型达mAP@50 94.8%，在复杂光照下表现稳定。
- 公开数据集与代码，适合农业视觉研究者使用。
蓝莓在自然环境中的检测因光照变化、遮挡和运动模糊而困难，深度学习检测器虽具潜力，但需大规模多样数据支持。本研究构建了一个包含661张树冠图像的新数据集（2022–2023年采集），含85,879个标注实例（其中36,256个成熟，49,623个未成熟），覆盖多种光照、遮挡及果实成熟度。评估了36个实时检测模型（包括YOLOv8-v12和RT-DETRv1-v2）。YOLOv12m达最高精度，mAP@50为93.3%；RT-DETRv2-X表现最佳，mAP@50达93.6%。中等规模模型在精度与速度间取得良好平衡。通过基于无偏均值教师的半监督学习（SSL），在2024年获取的1,035张未标注图像上微调，精度提升-1.4%至+2.9%，其中RT-DETRv2-X达94.8%。该数据集与软件已公开，推动精准果园管理研究。
原文摘要 · Abstract (English)
Blueberry detection in natural environments remains challenging due to variable lighting, occlusions, and motion blur due to environmental factors and imaging devices. Deep learning-based object detectors promise to address these challenges, but they demand a large-scale, diverse dataset that captures the real-world complexities. Moreover, deploying these models in practical scenarios often requires the right accuracy/speed/memory trade-off in model selection. This study presents a novel comparative benchmark analysis of advanced real-time object detectors, including YOLO (You Only Look Once) (v8-v12) and RT-DETR (Real-Time Detection Transformers) (v1-v2) families, consisting of 36 model variants, evaluated on a newly curated dataset for blueberry detection. This dataset comprises 661 canopy images collected with smartphones during the 2022-2023 seasons, consisting of 85,879 labelled instances (including 36,256 ripe and 49,623 unripe blueberries) across a wide range of lighting conditions, occlusions, and fruit maturity stages. Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%, while RT-DETRv2-X obtained a mAP@50 of 93.6%, the highest among all the RT-DETR variants. The inference time varied with the model scale and complexity, and the mid-sized models appeared to offer a good accuracy-speed balance. To further enhance detection performance, all the models were fine-tuned using Unbiased Mean Teacher-based semi-supervised learning (SSL) on a separate set of 1,035 unlabeled images acquired by a ground-based machine vision platform in 2024. This resulted in accuracy gains ranging from -1.4% to 2.9%, with RT-DETR-v2-X achieving the best mAP@50 of 94.8%. More in-depth research into SSL is needed to better leverage cross-domain unlabeled data. Both the dataset and software programs of this study are made publicly available to support further research.
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