Jacquard V2: Refining Datasets using the Human In the Loop Data
  Correction Method
Qiuhao Li Shenghai Yuan
In the context of rapid advancements in industrial automation, vision-based
robotic grasping plays an increasingly crucial role. In order to enhance visual
recognition accuracy, the utilization of large-scale datasets is imperative for
training models to acquire implicit knowledge related to the handling of
various objects. Creating datasets from scratch is a time and labor-intensive
process. Moreover, existing datasets often contain errors due to automated
annotations aimed at expediency, making the improvement of these datasets a
substantial research challenge. Consequently, several issues have been
identified in the annotation of grasp bounding boxes within the popular
Jacquard Grasp. We propose utilizing a Human-In-The-Loop(HIL) method to enhance
dataset quality. This approach relies on backbone deep learning networks to
predict object positions and orientations for robotic grasping. Predictions
with Intersection over Union (IOU) values below 0.2 undergo an assessment by
human operators. After their evaluation, the data is categorized into False
Negatives(FN) and True Negatives(TN). FN are then subcategorized into either
missing annotations or catastrophic labeling errors. Images lacking labels are
augmented with valid grasp bounding box information, whereas images afflicted
by catastrophic labeling errors are completely removed. The open-source tool
Labelbee was employed for 53,026 iterations of HIL dataset enhancement, leading
to the removal of 2,884 images and the incorporation of ground truth
information for 30,292 images. The enhanced dataset, named the Jacquard V2
Grasping Dataset, served as the training data for a range of neural networks.