Grasp detection within unstructured environments encounters challenges that lead to a reduced success rate in grasping attempts, attributable to factors including object uncertainty, random positions, and differences in perspective. This work proposes a grasp detection algorithm framework, Swin-transNet, which adopts a hypothesis treating graspable objects as a generalized category and distinguishing between graspable and non-graspable objects. The utilization of the Swin transformer module in this framework augments the feature extraction process, enabling the capture of global relationships within images. Subsequently, the integration of a decoupled head with attention mechanisms further refines the channel and spatial representation of features. This strategic combination markedly improves the system’s adaptability to uncertain object categories and random positions, culminating in the precise output of grasping information. Moreover, we elucidate their roles in grasping tasks. We evaluate the grasp detection framework using the Cornell grasp dataset, which is divided into image and object levels. The experiment indicated a detection accuracy of 98.1% and a detection speed of 52 ms. Swin-transNet shows robust generalization on the Jacquard dataset, attaining a detection accuracy of 95.2%. It demonstrates an 87.8% success rate in real-world grasping testing on a visual grasping system, confirming its effectiveness for robotic grasping tasks.
KEYWORDS: Diffusion, Image processing, Denoising, Image enhancement, Image filtering, Image analysis, Digital imaging, Signal to noise ratio, Signal processing, Linear filtering
Diffusion has received a lot of attention and has experienced significant developments, it can simultaneously enhance,
sharpen and denoise image. The diffusion coefficient is locally adjust according to image features such as edges, textures,
and moments, so it has many formats diffusion process according to the set of gradient. Suck as P-M diffusion and total
variation. The aim of the present paper is to study the total variation then replace the smoothed intensity function with
the P-M diffusion function and derive the fidelity term to get a novel nonlinear anisotropic P-M diffusion. And then
deduce the new diffusion application in discrete two dimension space for image denoise. The results of experiments
demonstrate the novel P-M diffusion denoise the image and retain the details more effective than traditional P-M
diffusion.
The diffusion process can simultaneously enhance, sharpen and denoise image. The diffusion coefficient is locally adjust according to image gradient, so it has many formats diffusion process according to the set of criteria, suck as P-M diffusion,complex diffusion and forward and backward diffusion. In the complex diffusion, the imaginary part of image serve as approximate second derivative of image. So using the imaginary to control the diffusion coefficient can combine the forward and backward complex diffusion.The forward and backward complex diffusion choice forward or backward diffusion according to the imaginary part. The forward diffusion denoise and smooth the image,and the backward diffusion magnify the noise and sharpen the edges, so the the forward and backward complex diffusion can't denoise detail part of image effectively.The shock filer sharpen the edges can take for inverse diffusion, in the paper,we use the imaginary part to control the shock filter as the backward part and the nonlinear complex diffusion as the forward part to combine a novel forward and backward complex diffusion. This novel isn't selectivity diffusion but forward and backward diffusion both diffuse simultaneously.The results of experiments demonstrate the novel denoise the image and retain the details more effective than the forward and backward complex diffusion.
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