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

• URL: http://arxiv.org/abs/2310.19223v1
• Date: Mon, 30 Oct 2023 02:01:49 GMT
• Title: Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images
• Authors: Zhaocong Li
• Abstract summary: This paper introduces an interesting approach to detect grasping pose from a single RGB image. We propose a modular learning network augmented with grasp detection and semantic segmentation. We demonstrate the feasibility and accuracy of our proposed approach through practical experiments and evaluations.
• Score: 2.759223695383734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection [22.60228799622782]
  Key bottleneck in object detection in IR images is lack of sufficient labeled training data. We seek to leverage cues from the RGB modality to scale object detectors to the IR modality, while preserving model performance in the RGB modality. We first pretrain these factor matrices on the RGB modality, for which plenty of training data are assumed to exist and then augment only a few trainable parameters for training on the IR modality to avoid over-fitting.
  arXiv  Detail & Related papers  (2023-09-28T16:55:52Z)
• AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion [1.8820731605557168]
  We propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net) In the encoding stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales. In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction.
  arXiv  Detail & Related papers  (2023-09-04T14:16:08Z)
• SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection [5.2134203335146925]
  RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. Despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. We propose a novel superpixel prototype sampling network architecture to solve this problem.
  arXiv  Detail & Related papers  (2022-07-16T10:43:14Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network [70.53093934205057]
  3D object detection task from lidar or camera sensors is essential for autonomous driving. We propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models.
  arXiv  Detail & Related papers  (2022-07-12T12:35:34Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild [48.44194221801609]
  We propose a new lightweight and end-to-end learning-based framework to tackle this challenge. We progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective camera spectral response function estimation. Our method significantly outperforms state-of-the-art unsupervised methods and even exceeds the latest supervised method under some settings.
  arXiv  Detail & Related papers  (2021-08-15T05:19:44Z)
• Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection [63.18846475183332]
  We aim to develop an efficient and compact deep network for RGB-D salient object detection. We propose a progressively guided alternate refinement network to refine it. Our model outperforms existing state-of-the-art approaches by a large margin.
  arXiv  Detail & Related papers  (2020-08-17T02:55:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.