# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 2 Related Works  
### Object Detection  
Object detection plays a very important role in the field of machine vision, which aims to detect instances of visual objects of certain classes in images. That is, the purpose of object detection is to accurately locate objects in images by using effective detection algorithms. Currently, most existing object detection approaches are mainly based on various deep network models, and they can be roughly classified into two categories: convolutional neural network (CNN)-based detection approaches and transformer-based detection approaches. In general, the former has a lower computational cost than the latter. In the following, we briefly review some important representative works in deep object detection models based on both CNNs and transformers. A representative method for object detection is the Fast-RCNN, first proposed by [PERSON] in [16], which utilizes a deep convolutional neural network (DCNN) [17] with multi-task loss functions to efficiently classify the object proposals and accelerates the detection model by using the classical truncated singular value decomposition strategy. After that, [PERSON] et al. [18] presented the well-known Faster-RCNN by applying a region proposal network to produce several candidate boxes, significantly improving the quality of candidate boxes, as well as showing high detection accuracy. Another representative work is YOLO, presented by [PERSON] et al. in [19], which is designed with the purpose of completing object detection in a single forward pass, eliminating the computational burden of traditional multi-step detection processes. Due to their simple structure, high accuracy, and low computational costs, YOLO and its variants have been widely used to deal with various detection tasks and achieved excellent detection performance in practical applications. Specifically, YOLOv1 [19] is the first generation of the YOLO series, introducing an end-to-end object detection approach. It divides an input image into a fixed number of grid cells and predicts classes and bounding boxes within each grid cell. YOLOv2 [20] introduces anchor boxes to handle objects of different sizes and adopts deeper network architectures, i.e., Darknet-19 and Darknet-53. Furthermore, YOLOv3 [21] improves the detection performance by using more convolutional layers and reducing the size of the network stride. In particular, it employs three different scales of feature maps to capture features at different levels and also introduces cross-scale connections and multi-scale predictions to enhance detection performance for small objects. The transformer presented in [22] was originally designed for natural language processing. It works very well in modeling long-term dependencies in time series. Many recent works [23; 24; 25; 26; 27] have extended it to deal with various computer vision tasks. [PERSON] et al. [24] innovatively constructed a transformer-based object detection model called DETR by redesigning the object detection network structure. Specifically, it first applies CNNs to capture image features and aggregates these features with positional encodings, and then it passes them to the transformer's encoder. Next, a set of object queries and the output of this encoder are processed together in the decoder. Finally, each output of the transformer's decoder is passed through a feedforward network (FFN) to independently decode it into box coordinates and the corresponding class labels. In essence, DETR translates object detection into a prediction problem, which can simplify the overall process of object detection. Based upon the relationship between objects and the global context, it directly produces the final prediction set in parallel. Furthermore, [PERSON] et al. [28] proposed the deformable DETR, which integrates the advantages of deformable convolution in sparse spatial sampling and the transformer in relationship modeling. In [29], [PERSON] et al. studied the limitations of the DETR model.