The visual recognition of green fruits in natural environments is always a difficult problem for agricultural robots due to the colour similarity between fruits and background. This study proposed a green fruit detection method named deep bounding box regression forest (DBBRF) for detecting green citrus in natural environments. First, probabilistic class labels were designed for image patches used as training samples for the forest model. Then, objective functions were used alternately while constructing the single-layer regression forest to reduce the class uncertainty and bounding box uncertainty of the object. Furthermore, the output of the model, taken as the new features, was concatenated with the input features to train multiple regression forests. With regard to the feature extraction, a multi-scale fusion feature was designed to describe the citrus in different scales with three aspects of features, including shape, texture, and colour. By testing 800 randomly selected citrus images, the experimental results showed that the average execution time of the method was 0.759 s and that the mAP was 87.6%. This study provides technical support for green fruit detection in natural environments.

中文翻译：

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一种基于深度边界框回归森林的绿色柑橘检测方法

由于水果和背景之间的颜色相似，自然环境中绿色水果的视觉识别一直是农业机器人的难题。本研究提出了一种名为深度边界框回归森林（DBBRF）的绿色水果检测方法，用于检测自然环境中的绿色柑橘。首先，为用作森林模型训练样本的图像块设计了概率类标签。然后，在构建单层回归森林的同时交替使用目标函数，以减少对象的类别不确定性和边界框不确定性。此外，将模型的输出作为新特征与输入特征连接起来训练多元回归森林。关于特征提取，设计了一个多尺度融合特征，从形状、质地和颜色三个方面的特征来描述不同尺度的柑橘。通过对随机选取的 800 张柑橘图像进行测试，实验结果表明，该方法的平均执行时间为 0.759 s，mAP 为 87.6%。本研究为自然环境下的绿色水果检测提供技术支持。