In weed control, precision agriculture can help to greatly reduce the use of herbicides, resulting in both economical and ecological benefits. A key element is the ability to locate and segment all the plants from image data. Modern instance segmentation techniques can achieve this, however, training such systems requires large amounts of hand-labelled data which is expensive and laborious to obtain. Weakly supervised training can help to greatly reduce labelling efforts and costs. We propose panoptic one-click segmentation, an efficient and accurate offline tool to produce pseudo-labels from click inputs which reduces labelling effort. Our approach jointly estimates the pixel-wise location of all N objects in the scene, compared to traditional approaches which iterate independently through all N objects; this greatly reduces training time. Using just 10% of the data to train our panoptic one-click segmentation approach yields 68.1% and 68.8% mean object intersection over union (IoU) on challenging sugar beet and corn image data respectively, providing comparable performance to traditional one-click approaches while being approximately 12 times faster to train. We demonstrate the applicability of our system by generating pseudo-labels from clicks on the remaining 90% of the data. These pseudo-labels are then used to train Mask R-CNN, in a semi-supervised manner, improving the absolute performance (of mean foreground IoU) by 9.4 and 7.9 points for sugar beet and corn data respectively. Finally, we show that our technique can recover missed clicks during annotation outlining a further benefit over traditional approaches.

翻译：在杂草防治中，精准农业可大幅减少除草剂的使用，带来经济和生态双重效益。其关键要素在于能够从图像数据中定位并分割所有植物。现代实例分割技术可实现这一目标，但训练此类系统需要大量手工标注数据，成本高昂且耗费人力。弱监督训练有助于显著降低标注工作量与成本。我们提出"全景一键分割"方法，这是一种基于点击输入生成伪标签的高效准确离线工具，可减少标注投入。与传统方法需逐个独立遍历所有N个对象不同，本方法联合估计场景中所有N个对象的像素级位置，从而大幅缩短训练时间。仅用10%的数据训练我们的全景一键分割方法，在具有挑战性的甜菜和玉米图像数据上，平均对象交并比（IoU）分别达到68.1%和68.8%，性能与传统一键分割方法相当，但训练速度快约12倍。我们通过从剩余90%数据的点击操作生成伪标签，验证了系统的适用性。这些伪标签随后用于半监督方式训练Mask R-CNN，使甜菜和玉米数据的前景平均IoU绝对值分别提升9.4和7.9个百分点。最后，我们证明该技术可恢复标注过程中遗漏的点击，进一步凸显其相对于传统方法的优势。