  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%.
