论文标题
肚脐橙色细分的挑剔注意力网络
Fastidious Attention Network for Navel Orange Segmentation
论文作者
论文摘要
深度学习在许多领域都取得了出色的性能，因此我们不仅将其应用于肚脐橙色的语义细分任务，以解决区分缺陷类别并识别茎端和开花端的两个问题，而且还提出了一种挑剔的注意机制来进一步提高模型性能。这种轻巧的注意机制包括两个可学习的参数，激活和阈值，以捕获远程依赖性。具体而言，阈值挑选出空间特征图的一部分，激活使该区域激发了该区域。根据来自不同类型的特征图的激活和阈值训练，我们设计了挑剔的自我发场模块（FSAM）和挑剔的注意力间模块（FIAM）。然后构建使用U-NET作为骨架并嵌入这两个模块的挑剔的注意力网络（fanet），以解决用于茎端，开花端，缺陷和溃疡的语义分割的问题。与我们的肚脐橙色数据集下的一些最先进的基于深度学习的网络相比，实验表明，我们的网络是最佳性能，像素精度为99.105％，平均精度为77.468％，平均IU 70.375％和频率加权IU 98.335％。嵌入式模块显示出更好的歧视，包括背景，尤其是IU缺陷的5种类别，增加了3.165％。
Deep learning achieves excellent performance in many domains, so we not only apply it to the navel orange semantic segmentation task to solve the two problems of distinguishing defect categories and identifying the stem end and blossom end, but also propose a fastidious attention mechanism to further improve model performance. This lightweight attention mechanism includes two learnable parameters, activations and thresholds, to capture long-range dependence. Specifically, the threshold picks out part of the spatial feature map and the activation excite this area. Based on activations and thresholds training from different types of feature maps, we design fastidious self-attention module (FSAM) and fastidious inter-attention module (FIAM). And then construct the Fastidious Attention Network (FANet), which uses U-Net as the backbone and embeds these two modules, to solve the problems with semantic segmentation for stem end, blossom end, flaw and ulcer. Compared with some state-of-the-art deep-learning-based networks under our navel orange dataset, experiments show that our network is the best performance with pixel accuracy 99.105%, mean accuracy 77.468%, mean IU 70.375% and frequency weighted IU 98.335%. And embedded modules show better discrimination of 5 categories including background, especially the IU of flaw is increased by 3.165%.
