## Article  
### YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model  
[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2  
To ensure the safety of train operations, railway workers must periodically check the catenary droppers. Any railway line contains a large number of catenary droppers, making manual inspection inefficient, time-consuming, and prone to mistakes. Performing inspection operations at night makes it more difficult for the inspectors to check the droppers, placing higher requirements on the inspection techniques. To identify faults in catenary droppers, railway companies have developed a catenary monitoring device. This device takes pictures of the catenaries at close range and transmits them to the railway workers. The railway workers check the pictures one by one and judge whether there are defects in the catenary droppers. This method improves the detection efficiency, but three problems persist: firstly, there is a delay between image acquisition and fault identification; secondly, a large number of images is obtained, and manually checking all of the pictures is highly inefficient; and, thirdly, small defects are not easy to identify in the pictures, and may therefore be missed or misidentified. Thus, there is an urgent need to use digital image processing and deep learning to develop an intelligent detection technology that will effectively improve the detection efficiency and accuracy of catenary dropper defects. In pursuit of the intelligent detection of catenary defects, many scholars have studied the detection of various components of high-speed rail catenaries, such as insulator recognition and defect detection [4; 5], loose nut detection in U-shaped hoops [6], bird's nest detection [7], loose defect detection for bracing wire components [8], and arc detection and recognition in pantograph-catenary systems [9]. At present, research on catenary droppers tends to focus on engineering structures and geometric parameters, as well as the impact of the suspension string design on train operation safety at the physical level. For example, the inrush current characteristics [10], stress characteristics [11; 12], dynamic performance [13; 14], geometric characteristics [15], wind resistance [16], and fatigue analysis [17] of the droppers have recently been studied. To date, there has been relatively little research on the intelligent detection of dropper faults. Existing studies have mainly sought to ensure contact through dropper detection by improving the faster region-proposal convolutional neural network (R-CNN) and You Only Look Once (YOLOv3) algorithm. [PERSON] et al. [18] replaced DenseNet with the original feature extraction module of faster R-CNN to extract the features of the dropper image in order to extract the deep image features. By modifying the original feature extraction module of faster R-CNN to extract the characteristics of the dropper, it is possible to effectively combine deep and shallow feature maps [19; 20]. [PERSON] et al. [21] propose an improved faster R-CNN for OCS dropper detection, including the balanced attention feature pyramid network (BA-FPN) and center-point rectangle loss (CR loss), which can accurately recognize and locate droppers, while [PERSON] et al. [22] combined YOLOv3 and Odin to achieve dropper positioning and status detection. In [23], a feature pyramid consisting of five convolution layers with different scales was constructed to improve YOLOv3, but this was only applied to the positioning of droppers rather than the detection of defects. [PERSON] et al. [24] proposed a machine learning detection method based on time-frequency analysis, combining a support vector machine (SVM) and independent component analysis (ICA) to identify and locate broken droppers. These studies have undoubtedly made progress in the intelligent detection of contact network droppers, but they are mainly focused on the dropper positioning and status detection.