mlsanityData to 14 Oct 2026

Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images

arXiv 2310.19223 ↗First posted 30 Oct 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

While traditional methods relies on depth sensors, the current trend leans towards utilizing cost-effective RGB images, despite their absence of depth cues. This paper introduces an interesting approach to detect grasping pose from a single RGB image. To this end, we propose a modular learning network augmented with grasp detection and semantic segmentation, tailored for robots equipped with parallel-plate grippers. Our network not only identifies graspable objects but also fuses prior grasp analyses with semantic segmentation, thereby boosting grasp detection precision. Significantly, our design exhibits resilience, adeptly handling blurred and noisy visuals. Key contributions encompass a trainable network for grasp detection from RGB images, a modular design facilitating feasible grasp implementation, and an architecture robust against common image distortions. We demonstrate the feasibility and accuracy of our proposed approach through practical experiments and evaluations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 30 Oct 2023.

Builds on

19 of 43 references.

References · 19

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  5. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  6. 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
  7. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 314
  8. 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 209
  9. 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
  10. 2013Deep Learning for Detecting Robotic Graspscited by 116
  11. 2019Seamless Scene Segmentationcited by 112
  12. 2017In-Place Activated BatchNorm for Memory-Optimized Training of DNNscited by 81
7 more
  1. 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 76
  2. 2019EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasetscited by 58
  3. 2021End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBcited by 44
  4. 2018ROI-based Robotic Grasp Detection for Object Overlapping Scenescited by 31
  5. 2018Fully Convolutional Grasp Detection Network with Oriented Anchor Boxcited by 27
  6. 2022Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overviewcited by 16
  7. 2019A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detectioncited by 8

Built on by

Not cited yet.