The prevention and control of Camellia Oleifera diseases can produce significantly comprehensive economic benefits. And precisely identifying the types of diseases on oil tea leaves can enhance the effectiveness of pest and disease control. Currently, the most effective deep learning methods for segmentation are suffering from the challenges such as indistinct disease spots on the actual oil tea leaves that are difficult to differentiate from the background, the high similarities in characteristics between different diseases, and the small target area of disease regions that are easily ignored. In this paper, GS-DeepLabV3+ network is proposed, which is an improvement upon DeepLabV3+ and aims to effectively enhance disease identification and segmentation rates. Initially, a gated pyramid feature fusion structure is proposed, utilizing a special module to merge features of different dimensions to enhance differentiation of similar features. Secondly, an improved grouped attention fusion mechanism is introduced, which allocates channel and spatial weights to different feature groups, enhancing the extraction of detailed features. Lastly, we substituted the backbone network of DeepLabV3+ with the more lightweight MobileNetV2, which significantly reduces the number of parameters while more stably extracting multidimensional features. In comparison with the existing segmentation networks on a self-created oil tea leaf disease data set, GS-DeepLabV3+ achieved a mIoU of 87.77% and an average pixel accuracy of 94.55%. Compared to the base DeepLabV3+ network, there was a 4.14% improvement in mIoU, a 3.58% increase in mPA, and a 1.4% rise in precision. The improved attention mechanism contributed to a 1.98% increase in mIoU, and the gated multidimensional fusion mechanism resulted in a 1.03% improvement. These enhancements can provide technical references for crop protection and pest disease segmentation.

中文翻译：

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GS-DeepLabV3+：基于改进的Shuffle Attention和门控多维特征提取的山茶病害分割网络


油茶病害防治可产生显着的综合经济效益。准确识别油茶病害类型，可以提高病虫害防治的效果。目前，最有效的深度学习分割方法面临着实际油茶病斑不明显，难以与背景区分、不同病害特征相似度高、病害目标区域较小等挑战。容易被忽视的疾病部位。本文提出了GS-DeepLabV3+网络，它是对DeepLabV3+的改进，旨在有效提高疾病识别和分割率。最初，提出了一种门控金字塔特征融合结构，利用特殊的模块来合并不同维度的特征，以增强相似特征的区分度。其次，引入了改进的分组注意力融合机制，将通道和空间权重分配给不同的特征组，增强了细节特征的提取。最后，我们用更轻量级的MobileNetV2替代了DeepLabV3+的主干网络，显着减少了参数数量，同时更稳定地提取多维特征。在自建的油茶叶病数据集上与现有分割网络相比，GS-DeepLabV3+的mIoU达到87.77%，平均像素精度达到94.55%。与基础 DeepLabV3+ 网络相比，mIoU 提高了 4.14%，mPA 提高了 3.58%，精度提高了 1.4%。改进的注意力机制促成了 1。mIoU 提升了 98%，门控多维融合机制带来了 1.03% 的提升。这些增强功能可为作物保护和病虫害细分提供技术参考。