FOCRD: Fully Occluded Citrus Radar Dataset

In this dataset, a comprehensive collection of high-fidelity radar signatures is presented to address the critical challenges of autonomous citrus perception. This dataset was generated during a 45-day intensive data collection period at the University of Wollongong, within the Applied Mechatronics and Biomedical Engineering Research (AMBER) group. All scans were performed in a controlled laboratory environment using real citrus fruits and genuine foliage to capture precise radar responses for agricultural robotics research. The primary goal of this collection is to provide a reliable sensing alternative for citrus detection and localisation where traditional vision-based systems fail due to 100% visual occlusion. The dataset architecture is fully compatible with shuffling and curriculum learning strategies to support diverse model training requirements. Dataset Organisation (Curriculum Phases):The data is organised into three complexity levels to support hierarchical learning strategies as defined in the dataset's splitting configuration: Phase 1 (Easy): Consists of unoccluded citrus and background samples. Phase 2 (Medium): Includes unoccluded citrus along with only_leaf and leaf+branch negatives. Phase 3 (Hard): Represents the full set and introduces the most challenging scenarios with fully occluded citrus targets hidden behind dense foliage, alongside background, only_leaf, and leaf+branch samples. Technical Acquisition & Format: Sensor & Hardware: 60GHz Acconeer A121 pulsed coherent radar mounted on a custom 2-DOF pan-tilt system, covering a 50° azimuth and 35° elevation field of view. Data Processing: Raw IQ signals are processed into 36 × 51 radar intensity images using a temporal-variance filter to suppress dynamic reflections from foliage. Metadata Sync: Each of the 931 unique scans is synchronized with a 4D metadata vector: [Grid ID, Total Distance, Sensor-to-Leaf Distance, Foliage Freshness]. Regional Compensation: Grid ID covers 9 sectors (16.6° × 11.6°) to account for angular leakage. Environmental Parameters: Includes an angular step size of 1°, range resolution of 0.0025 m across 160 bins, and leaf thicknesses ranging from 0.8 to 1.4 mm. Data Partitioning & Augmentation: Splits: Training, validation, and testing subsets are partitioned in a 50:25:25 ratio. Augmentation: Training data is augmented through offline horizontal flipping, involving a synchronized transformation of intensity maps, bounding-box coordinates, and spatial Grid IDs to maintain physical context consistency across the symmetric azimuth plane. Significance:The inclusion of the 4D context vector allows for a deep analysis of environmental disturbers and signal attenuation, offering a solution to the fundamental challenges of heavy occlusion in orchard environments. This structure makes the dataset suitable for testing robotic perception in real-world agricultural tasks such as precise harvesting and targeted spraying.本数据集收录了一系列高保真雷达特征信号（radar signatures），以应对柑橘自主感知领域的关键挑战。该数据集于伍伦贡大学应用机电与生物医学工程研究（AMBER）组内开展的为期45天的集中数据采集周期中生成。所有扫描均在受控实验室环境下完成，使用真实柑橘果实与天然枝叶，以获取适用于农业机器人研究的精准雷达响应。本数据集的核心目标是为柑橘检测与定位提供可靠的感知方案，解决传统视觉系统在完全视觉遮挡场景下失效的问题。该数据集架构完全兼容洗牌与课程学习策略，可适配多样化的模型训练需求。 数据集组织（课程学习阶段）：根据数据集的划分配置，数据被划分为三个复杂度等级以支持分层学习策略： 阶段1（简单）：包含无遮挡柑橘与背景样本。 阶段2（中等）：包含无遮挡柑橘，以及仅叶片、叶+枝两类负样本。 阶段3（困难）：涵盖完整样本集，并引入最具挑战性的场景：完全被茂密枝叶遮挡的柑橘目标，同时包含背景、仅叶片与叶+枝样本。 技术采集与数据格式： 传感器与硬件：采用安装于定制二自由度（2-DOF）云台系统上的60GHz Acconeer A121脉冲相干雷达，覆盖50°方位角与35°俯仰视场。 数据处理：原始IQ信号经时域方差滤波器处理，抑制枝叶产生的动态反射，最终生成36×51的雷达强度图像。 元数据同步：931次独立扫描均与四维元数据向量同步，该向量为[网格ID、总距离、传感器至叶片距离、枝叶新鲜度]。 区域补偿：网格ID覆盖9个扇区（16.6°×11.6°），以抵消角度泄漏的影响。 环境参数：包含1°的角度步长、160个分辨率为0.0025m的距离仓，以及厚度介于0.8mm至1.4mm的枝叶样本。 数据划分与增强： 划分比例：训练集、验证集与测试集按照50:25:25的比例划分。 数据增强：训练数据通过离线水平翻转进行增强，同步对强度图、边界框坐标与空间网格ID进行变换，以维持对称方位平面上的物理上下文一致性。 数据集意义：四维上下文向量的加入支持对环境干扰与信号衰减的深度分析，为果园环境中重度遮挡这一核心挑战提供了解决方案。该数据集结构适用于测试机器人在实际农业任务中的感知能力，例如精准采摘与定向喷雾。