Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #18 on 3D Semantic Segmentation on SemanticKITTI
We count single berries in images to avoid the error-prone detection of grapevine clusters.
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #19 on 3D Semantic Segmentation on SemanticKITTI
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.
Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.
We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions.