# Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection

arXiv: https://arxiv.org/abs/2101.10226 · HTML: https://mlsanity.com/paper/2101.10226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 30 of 30)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [ROI-based Robotic Grasp Detection for Object Overlapping Scenes](https://mlsanity.com/paper/1808.10313/) (2018)
- [Fully Convolutional Grasp Detection Network with Oriented Anchor Box](https://mlsanity.com/paper/1803.02209/) (2018)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review](https://mlsanity.com/paper/1905.06658/) (2019)
- [Densely Supervised Grasp Detector (DSGD)](https://mlsanity.com/paper/1810.03962/) (2018)
- [Classification based Grasp Detection using Spatial Transformer Network](https://mlsanity.com/paper/1803.01356/) (2018)
- [GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier](https://mlsanity.com/paper/1903.02489/) (2019)
- [Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images](https://mlsanity.com/paper/1902.08950/) (2019)
- [Scoring Graspability based on Grasp Regression for Better Grasp Prediction](https://mlsanity.com/paper/2002.00872/) (2020)
- [Real-world Multi-object, Multi-grasp Detection](https://mlsanity.com/paper/1802.00520/) (2018)
- [Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images](https://mlsanity.com/paper/1809.05828/) (2018)
- [Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation](https://mlsanity.com/paper/2006.05123/) (2020)
- [SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper](https://mlsanity.com/paper/2005.08222/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)

## Abstract (arXiv)

The method of deep learning has achieved excellent results in improving the performance of robotic grasping detection. However, the deep learning methods used in general object detection are not suitable for robotic grasping detection. Current modern object detectors are difficult to strike a balance between high accuracy and fast inference speed. In this paper, we present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation from an n-channel input image of the real grasping scene. The proposed network is a lightweight generative architecture for grasping detection in one stage. Specifically, a grasping representation based on Gaussian kernel is introduced to encode training samples, which embodies the principle of maximum central point grasping confidence. Meanwhile, to extract multi-scale information and enhance the feature discriminability, a receptive field block (RFB) is assembled to the bottleneck of our grasping detection architecture. Besides, pixel attention and channel attention are combined to automatically learn to focus on fusing context information of varying shapes and sizes by suppressing the noise feature and highlighting the grasping object feature. Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed. The network is an order of magnitude smaller than other excellent algorithms while achieving better performance with an accuracy of 98.9% and 95.6% on the Cornell and Jacquard datasets, respectively.