WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image.

ABSTRACT: Automatically segmenting crops and weeds in the image input from cameras accurately is essential in various agricultural technology fields, such as herbicide spraying by farming robots based on crop and weed segmentation information. However, crop and weed images taken with a camera have motion blur due to various causes (e.g., vibration or shaking of a camera on farming robots, shaking of crops and weeds), which reduces the accuracy of crop and weed segmentation. Therefore, robust crop and weed segmentation for motion-blurred images is essential. However, previous crop and weed segmentation studies were performed without considering motion-blurred images. To solve this problem, this study proposed a new motion-blur image restoration method based on a wide receptive field attention network

SUBMITTER: Yun C 

PROVIDER: S-EPMC10243196 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature