用边界损失提升植物叶片与杂草的分割精度，助力精准农业
Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves
- 结合焦点损失与边界损失优化小目标分割
- 在标准数据集上实现81.89的PQ+指标
- 可同时精确计数叶片，适合农业监测场景
精准农业通过数据与机器学习技术，使农民能精准监控作物并针对性干预，仅对杂草施药或对缺肥作物施肥，从而最大化产量并减少资源浪费与环境影响。为此，我们提出一种分层全景分割方法，可同时确定叶片数量（作为植株生长标识）并定位杂草。该方法通过引入焦点损失和边界损失，显著提升叶片与杂草等小目标的分割效果。不仅达到具有竞争力的性能，在标准训练集上取得81.89的PQ+指标，还验证了其在叶片计数上的准确率提升。代码已开源：https://github.com/madeleinedarbyshire/HierarchicalMask2Former。
原文摘要 · Abstract (English)
Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds, or the precision application of fertilizer only to undernourished crops, rather than to the entire field. The approach promises to maximize yields while minimizing resource use and harm to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method that simultaneously determines leaf count (as an identifier of plant growth)and locates weeds within an image. In particular, our approach aims to improve the segmentation of smaller instances like the leaves and weeds by incorporating focal loss and boundary loss. Not only does this result in competitive performance, achieving a PQ+ of 81.89 on the standard training set, but we also demonstrate we can improve leaf-counting accuracy with our method. The code is available at https://github.com/madeleinedarbyshire/HierarchicalMask2Former.
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