# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 1 Introduction  
Pointer meters are important instruments for monitoring the status of operation devices. They have been widely used in industrial production due to their reliability and low cost [1]. The current way of reading a pointer meter is manual, which leads to issues such as low efficiency, poor accuracy, and high labor costs. In particular, manual meter reading in an unfavorable environment may lead to safety accidents [2]. Therefore, automatic and accurate pointer meter reading recognition is a natural solution, which is very desirable in industry. With the recent development of deep learning techniques, many methods based on deep learning have been developed in the literature to handle various tasks [3; 4; 5; 6], particularly automatic pointer meter reading. For example, [PERSON] et al. [7] presented a deep network model for automatic meter reading detection that employs a convolutional network equipped with a non-linear network-in-network (NiN) kernel [8] and uses global regression to locate the pointer and reference points. The main goal of this model is to accurately detect the pointer position and all symbols of the meter dial. However, in practice, some symbols are not necessary for meter reading recognition, which may have a negative influence on accurate meter reading. In [9], a modified Mask-RCNN [10] with a precise RoI pooling (PrRoIPooling) [11] operation was developed that can identify the type of pointer meter and also fit the binary mask of the pointer and meter dial. Unlike these two methods, [PERSON] et al. [12] focused on detecting the pointers of meters by using a method that takes the pointer as a 2D vector and utilizes a vector detection network to estimate the confidence map of this vector. The resulting vector is then used for the reading calculation by template matching and coordinate projection techniques. Different from the methods mentioned above that employ object detectors to locate the pointer, some works determine the pointer position by using various deep segmentation networks. For example, [PERSON]. [13] presented an attention U-Net for segmenting the pointer from the meter dial obtained by an object detector. Meanwhile, [PERSON] et al. [14] designed a lightweight semantic segmentation model for fully automatic pointer recognition that leverages a context-guided network and a channel attention mechanism. In addition, in order to further improve the accuracy of reading recognition, various enhancement strategies, such as image skew correction and bilateral filtering for meter images, are discussed in [15]. Even though there have been many deep learning-based pointer meter reading models, the reading accuracy of these models is still unable to meet the requirements of real-world applications. To overcome this limitation, we present a three-stage method for tackling the pointer meter reading recognition task, which is composed of meter dial location, reference point segmentation, and meter dial reading. The meter dial location stage aims to quickly locate the meter dials and output the corresponding bounding boxes. Different from the task of object location in a complex scene, meter dial location in industrial applications is generally easier to process. Therefore, we adopt a lightweight yet effective object detection network in this stage. In the segmentation stage, the dial region image determined by the bounding box is inputted into a deep semantic segmentation network. Then, the pointer and reference points from this segmentation network are used to compute the dial number in the meter dial reading stage. Due to it being robust to image distortion, we utilize the relative position, not the angle, between the pointer and reference points to obtain the final value of the meter dial. Additionally, we also collected 380 m images from the internet and two manufacturing plants, of which 304 images were used to train the model, and the remaining ones were used for testing. The remainder of this work is organized as follows.