# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 2 Related Works
### Semantic Segmentation  
The second stage in our model is to segment the pointer and reference points from the meter dial by the semantic segmentation technique, which is also a fundamental task in the fields of image processing and computer vision. Thus, some important deep models for semantic segmentation are reviewed in this subsection. Two representative semantic segmentation models are the fully convolutional network (FCN) [32] and U-Net [33]. Due to its great success in handling the semantic segmentation task, the FCN has been a popular backbone, in which a skip connection is introduced to combine low-level detail information with high-level semantic information. The skip connection is very beneficial for preserving the boundary information of images and thus improving the segmentation performance. Furthermore, U-Net [33] and its variants [34] use dense skip connections to fuse the features of the encoder and the corresponding features of the decoder and achieve significant performance improvements in medical image segmentation. Different from the FCN and U-Net, the SegNet model, first proposed in [35; 36], introduces an index preserving max pooling to compensate for the reduced spatial resolution caused by the max pooling operation used in the encoder. In addition, DeepLab and various improved versions employ dilated convolution to increase the receptive fields of convolution operations and also utilize spatial pyramid pooling to extract multi-scale contextual information, achieving a significant performance gain over previous semantic segmentation models. In order to aggregate the global information and further improve the segmentation performance, ref. [37] proposed a hybrid dilated convolution approach that can effectively increase the encoder's receptive fields and also mitigate the gridding issue caused by traditional dilated convolution. Multi-scale feature extraction is also very important for dealing with various semantic segmentation tasks, which can be implemented in different manners. A simple implementation approach is to directly feed multiple input images (i.e., different-resolution versions of an original input image) with different resolutions into one CNN. This method enables the network to extract discriminative multi-scale features [38]. Another way is to first construct a pyramid or steerable pyramid representation of an image [39] and then apply CNNs to process and fuse the feature maps at different scales. In [40], a pyramid pooling module was designed to fuse the local detail information and the global contextual information for effective feature extraction. Similarly, [PERSON] et al. [41] developed atrous spatial pyramid pooling. To better integrate multi-scale feature maps, RefineNet [42] presented a multi-resolution fusion block for accurately producing the segmentation result, in which the feature maps of different resolutions are fused by the skip connection and convolution operations. Unlike the aforementioned segmentation models that use specially designed network structures, a neural architecture search strategy was presented in [43] to adaptively identify and combine multiple branches with different resolutions. Meanwhile, some works attempted to enhance semantic segmentation models using the attention mechanism [44]. For example, a feature pyramid attention module was developed in [45] by combining the feature pyramid representation and attention mechanism, which resulted in more accurate segmentation results. In [46], the self-attention mechanism was introduced to deal with the semantic segmentation task, in which a position attention module is adopted to fuse useful spatial features scattered at different positions, and a channel attention module is also used to focus on interdependent feature maps. In order to capture long-range contextual information, a point-wise network based on spatial attention was proposed in [47], which improves the segmentation performance via bi-directional information propagation.