# Fully Convolutional Grasp Detection Network with Oriented Anchor Box

arXiv: https://arxiv.org/abs/1803.02209 · HTML: https://mlsanity.com/paper/1803.02209/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 27 of 27)

- [GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping](https://mlsanity.com/paper/2403.09637/) (2024)
- [Bird's-Eye-View Scene Graph for Vision-Language Navigation](https://mlsanity.com/paper/2308.04758/) (2023)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [MonoGraspNet: 6-DoF Grasping with a Single RGB Image](https://mlsanity.com/paper/2209.13036/) (2022)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [On Robot Grasp Learning Using Equivariant Models](https://mlsanity.com/paper/2306.06489/) (2023)
- [Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2403.18546/) (2024)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention](https://mlsanity.com/paper/2302.14264/) (2023)
- [GKNet: grasp keypoint network for grasp candidates detection](https://mlsanity.com/paper/2106.08497/) (2021)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [Perceiving Unseen 3D Objects by Poking the Objects](https://mlsanity.com/paper/2302.13375/) (2023)
- [I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches](https://mlsanity.com/paper/2205.04026/) (2022)
- [Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks](https://mlsanity.com/paper/2111.11114/) (2021)
- [Open Arms: Open-Source Arms, Hands & Control](https://mlsanity.com/paper/2205.12992/) (2022)
- [NBMOD: Find It and Grasp It in Noisy Background](https://mlsanity.com/paper/2306.10265/) (2023)
- [GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning](https://mlsanity.com/paper/2409.14403/) (2024)
- [Double-Dot Network for Antipodal Grasp Detection](https://mlsanity.com/paper/2108.01527/) (2021)
- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)
- [GI-NNet & RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigms](https://mlsanity.com/paper/2107.07452/) (2021)
- [FViT-Grasp: Grasping Objects With Using Fast Vision Transformers](https://mlsanity.com/paper/2311.13986/) (2023)
- [A Segmented Robot Grasping Perception Neural Network for Edge AI](https://mlsanity.com/paper/2507.13970/) (2025)
- [Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation](https://mlsanity.com/paper/2512.21065/) (2025)
- [Real-World Semantic Grasp Detection Based on Attention Mechanism](https://mlsanity.com/paper/2111.10522/) (2021)
- [Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images](https://mlsanity.com/paper/2310.19223/) (2023)
- [Robust Loss Functions for Object Grasping under Limited Ground Truth](https://mlsanity.com/paper/2409.05742/) (2024)

## Abstract (arXiv)

In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi-grasp predictor. The feature extractor is a deep convolutional neural network. The multi-grasp predictor regresses grasp rectangles from predefined oriented rectangles, called oriented anchor boxes, and classifies the rectangles into graspable and ungraspable. On the standard Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on image-wise split and object-wise split respectively, and outperforms the latest state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise split.