Panoptic One-Click Segmentation: Applied to Agricultural Data
This work addresses the high cost of hand-labeled data for precision agriculture, offering a tool to reduce labeling effort, though it is incremental as it builds on existing weakly supervised methods.
The paper tackles the problem of reducing labeling effort for instance segmentation in agricultural weed control by proposing panoptic one-click segmentation, which efficiently generates pseudo-labels from clicks, achieving 68.1% and 68.8% mean IoU on sugar beet and corn data with 12x faster training and improving Mask R-CNN performance by 9.4 and 7.9 points in semi-supervised setups.
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.