MFENet: Multi-scale feature extraction network for images deblurring and segmentation of swinging wolfberry branch

Abstract

Wolfberry harvesting faces challenges related to factors such as wind-induced branch movement and mechanical arm collisions, which lead to undesirable swinging of wolfberry branches. This motion results in blurred images captured by imaging devices, which hampers the effectiveness of the harvesting robot arm in identifying and grasping the wolfberry branches. Clear, unblurred images are imperative for precise branch recognition and successful harvesting. Unfortunately, prior research on wolfberry branch identification has largely overlooked the issue of motion blur. In this study, we propose a novel solution by introducing the Multi-Scale Feature Extraction Network (MFENet) specifically tailored for image deblurring tasks. Our approach aims to enhance the accuracy of identifying and tracking dynamically moving wolfberry branches. MFENet achieves this by harnessing a combination of multi-scale image content features, incorporating modules for multi-scale depth-separable convolution, multi-scale receptive field structure, and attention refinement residual processing. In this study, we propose a novel solution by introducing the Multi-Scale Feature Extraction Network (MFENet) specifically tailored for image deblurring tasks. Our approach aims to enhance the accuracy of identifying and tracking dynamically moving wolfberry branches. MFENet achieves this by harnessing a combination of multi-scale image content features, incorporating modules for multi-scale depth-separable convolution, multi-scale receptive field structure, and attention refinement residual processing. Our findings underscore the efficacy of the proposed MFENet in addressing the challenge of blurred images resulting from the swinging motion of wolfberry branches. Furthermore, through experimental validation, we demonstrate that the improved SparseInst segmentation method achieves exceptional precision and faster segmentation speeds when applied to the branch dataset. This research not only provides a practical solution to mitigate branch swinging during wolfberry harvesting but also offers valuable theoretical and technical support to advance this field.