Purpose

Fruit detection and counting represent one of the most important steps toward yield estimation and a well-known practice for farmers, on which they base the management of the harvesting, storage, and distribution phases of agricultural products. In the era of precision agriculture, yield estimation, which was previously performed only by human operators, is currently being re-designed through the employment of Artificial Intelligence and Computer Vision techniques. Despite the impressive results that AI has demonstrated in fruit detection systems, they rely on large image datasets, whose availability is still limited if compared to the great number of crop typologies. For this reason, great interest has recently been devoted to weakly supervised algorithms, which can reduce the dataset annotation effort required by using simple image-level labels.

Method

Based on these considerations, this work proposes a new method relying on a sample-efficient weakly supervised approach. The proposed system, named MangoDetNet, is trained through a two-stage curriculum learning approach, first involving an image reconstruction task, and secondly an image binary classification task for heatmap generation. In particular, during the first stage, the network is trained in an unsupervised manner for the image reconstruction task, in order to promote the learning of robust feature extractors that are customized for the fruit scenarios. The second stage of training, instead, is performed to achieve image binary classification, employing presence/absence binary labels. This phase further refines the feature extractor from the previous stage and favors the computation of more refined and precise activation maps.

Conclusion

As demonstrated through the experimental campaign, performed on a mango orchard image dataset, MangoDetNet is able to outperform the state-of-the-art weakly supervised approaches, providing an F1 score equal to 0.861, which is on par with those of fully supervised methods, and an F1 score equal to 0.856 when halving the number of labeled samples needed for training.

中文翻译：

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MangoDetNet：一种新颖的标签效率弱监督水果检测框架

 目的

水果检测和计数是产量估算的最重要步骤之一，也是农民众所周知的做法，他们以此为基础对农产品的收获、储存和分配阶段进行管理。在精准农业时代，以前只能由人类操作员进行的产量估算，目前正在通过人工智能和计算机视觉技术的应用进行重新设计。尽管人工智能在水果检测系统中展示了令人印象深刻的结果，但它们依赖于大型图像数据集，与大量作物类型相比，其可用性仍然有限。因此，最近人们对弱监督算法产生了极大的兴趣，该算法可以减少使用简单图像级标签所需的数据集注释工作。

 方法

基于这些考虑，这项工作提出了一种依赖于样本有效的弱监督方法的新方法。所提出的系统名为 MangoDetNet，通过两阶段课程学习方法进行训练，首先涉及图像重建任务，其次涉及用于生成热图的图像二元分类任务。特别是，在第一阶段，网络以无监督的方式进行图像重建任务的训练，以促进针对水果场景定制的鲁棒特征提取器的学习。相反，执行训练的第二阶段是为了实现图像二元分类，使用存在/不存在二元标签。此阶段进一步细化前一阶段的特征提取器，并有利于计算更细化和精确的激活图。

 结论

正如在芒果园图像数据集上进行的实验活动所证明的那样，MangoDetNet 能够超越最先进的弱监督方法，提供等于 0.861 的 F1 分数，与完全监督方法的分数相当，当训练所需的标记样本数量减半时，F1 分数等于 0.856。