## 1 Introduction  
As critical components of rail fasteners, the reliability of fastener screws is important for railways. The workers' eye is usually used for accurately positioning the internal combustion power screw wrench and the fastener screw, which is extremely low efficiency. Fastener screw maintenance robots that use computer vision technology can accurately identify and locate fastener screws, as shown in Figure 1, which can considerably reduce the work intensity and improve efficiency. The computing power of the equipment is restricted by the light weight, small size, and limited battery capacity of the robot, and it is challenging to keep high-performance devices working for a long time in various harsh environments. Therefore, a light-weight and high-performance fastener screw detection model is necessary to fulfill the detection task requirement. The deep-learning method has been widely used for object detection applications [1; 2; 3; 4]. Although two-stage approaches like R-CNN [5], Faster-RCNN [6; 7], and Mask-RCNN [8; 9] can achieve high detection accuracy, the computation cost is high, which impedes the usage in real-time fastener screw detection tasks. The one-stage methods, like the YOLO series [10; 11; 12; 13; 14], can obtain a fast detection speed with high accuracy. To date, the application of a fast fastener screw detection method using deep learning in the railway maintenance field is rare. Mushtaq et al. [15] proposed a screw recognition method based on deep learning and image processing, using the YOLOv5 algorithm to classify and recognize bolts, nuts, etc. Zhuang et al. [16] proposed a two-stage target recognition method based on deep learning, further improving the detection performance. The recognition task is on a railway and two kinds of fasteners. At the first stage, a modified YOLOv3 model is developed to provide the initial detection information. In the second stage, a domain logic-based hybrid model (DLHM) is introduced. The DLHM consists of the problem region calibration module and the symmetric region calibration module. He [17] proposed a nut localization and recognition method based on computer vision technology to accomplish automatic bolt assembly in automatic production lines. An industrial camera is used to supply the image of the nut, and then the image is processed using edge detection and Hough circle transformation to obtain the coordinates of the nut. Finally, the precise position of the target is obtained according to the nut's coordinates. In the field of model performance enhancement, Panigrahi et al. [18] proposed a DSM-IDM-YOLO, which integrates depth-wise separable convolution modules (DSMs) and an inception depth-wise convolution module (IDM) to capture a wide range of features of the target. These modules are incorporated into different layers of YOLOv2 to enhance the model performance. Based on YOLOv3, Ma et al. [19] proposed an AVS-YOLO, which can improve the model performance by introducing a densely connected feature pyramid and a scale-aware attention module. Jiang et al. [20] proposed a light-weight real-time object detection model. In the backbone section, they introduced a receptive field-extended backbone with a restricted number of convolution layers to extract informative features. In the neck section, the feature pyramid network (FPN) incorporates additional lateral connections to recycle features within each convolutional stage, and both the channel attention and spatial attention modules are introduced, thus boosting the model's performance. Yin et al. [21] proposed a fast detection technique for the condition of elderly fall action based on an enhanced version of YOLOv5s. Asymmetric convolution blocks (ACBs) are utilized in the backbone network instead of the original Conv to enhance the feature extraction process. Then, they introduced a spatial attention mechanism module to improve the detection accuracy. Liu et al. [22] proposed a distracted driving detection model based on YOLOv7.