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Found 7 papers, 2 papers with code
Video-based Automatic Lameness Detection of Dairy Cows using Pose Estimation and Multiple Locomotion Traits
The three most important traits were back posture measurement, head bobbing, and tracking distance.
Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes
We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning to overcome occlusion and improve perception.
Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts
Active vision is a promising approach to viewpoint planning, which helps robots to deliberately plan camera viewpoints to overcome occlusion and improve perception accuracy.
Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking
The accuracy of the representation was evaluated in a real-world environment, where successful representation and localisation of tomatoes in tomato plants were achieved, despite high levels of occlusion, with the total count of tomatoes estimated with a maximum error of 5. 08% and the tomatoes tracked with an accuracy up to 71. 47%.
Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts
Through simulation experiments using plants with high levels of occlusion and structural complexity, we showed that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction.
Active learning with MaskAL reduces annotation effort for training Mask R-CNN
In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes.
Improved Part Segmentation Performance by Optimising Realism of Synthetic Images using Cycle Generative Adversarial Networks
We hypothesised that the translated images can be used for (i) improved learning of empirical images, and (ii) that learning without any fine-tuning with empirical images is improved by bootstrapping with translated images over bootstrapping with synthetic images.
