Research on citrus segmentation algorithm based on complex environment

Abstract

Aiming to address the low efficiency of current deep learning algorithms for segmenting citrus in complex environments, this paper proposes a study on citrus segmentation algorithms based on a multi-scale attention mechanism. The DeepLab V3+ network model was utilized as the primary framework and enhanced to suit the characteristics of the citrus dataset. In this paper, we will introduce a more sophisticated multi-scale attention mechanism to enhance the neural network’s capacity to perceive information at different scales, thus improving the model’s performance in handling complex scenes and multi-scale objects. The DeepLab V3+ network addresses the challenges of low segmentation accuracy and inadequate refinement of segmentation edges when segmenting citrus in complex scenes, and the experimental results demonstrate that the improved algorithm in this paper achieves 96.8 % in the performance index of MioU and 98.4 % in the performance index of MPA, which improves the segmentation effectiveness to a significant degree.