## 1 Introduction
Utilizing the methods proposed based on MKD (multiple knowledge dimensions) visual analysis, they established a reference for the application and development of methodologies in the field of road safety research [11]. Some scholars have practically implemented a method named DEA-ANN (data envelopment analysis-artificial neural networks) within a geographic information system (GIS) environment to conduct road safety risk analysis [12]. Leveraging drones as tools for data acquisition and employing advancements in computer vision algorithms for traffic flow analysis, risk assessment, and infrastructure damage evaluation represent a novel approach in road safety research. However, this method involves airspace usage and faces numerous challenges in practical deployment [13; 14; 15; 16]. As elucidated from the above analysis, current scholarly research in the realm of highway accident analysis and prevention predominantly concentrates on three aspects: firstly, the analysis of driver behavior; secondly, the natural weather conditions and traffic flow on highways; and thirdly, the emergency response measures post-accidents. However, given the high sensitivity of highway traffic systems to even minor issues, small problems in road conditions can rapidly escalate into serious consequences. Therefore, from the viewpoint of highway administrators, there is an urgent need to develop a system capable of swiftly and precisely detecting and pinpointing any anomalies on highways, and that can immediately alert maintenance personnel for remedial action. Fortuitously, with the continuous evolution of artificial intelligence technology, the application of target detection techniques from deep learning in the identification of road surface information has commenced. This advancement is significantly instrumental in augmenting our monitoring and response capabilities to anomalies on highways, marking a critical progression in this field. The current landscape of deep learning-based object detection algorithms can be broadly categorized into two approaches: two-stage and single-stage methods [17]. Two-stage object detection algorithms operate in a bifurcated manner, initially generating candidate regions, followed by classification via convolutional neural networks. Representative algorithms in this category include R-CNN [18], Fast R-CNN [19; 20], and Faster R-CNN [21]. However, due to their inherent two-stage nature, these algorithms often exhibit slower detection speeds when dealing with large model sizes and numerous parameters. On the other hand, single-stage detection algorithms have effectively addressed these speed limitations. Notable among these are SSD [22; 23], RetinaNet [24], and YOLO [25; 26; 27; 28]. With the maturation of single-stage algorithms (e.g., YOLO), they have not only demonstrated superiority in detection speed compared to two-stage methods but also achieved comparable or even superior accuracy. Despite these advances, there are still significant challenges when applying such algorithms to roadway abnormal condition detection. Object detection tasks are typically executed by edge devices situated alongside highways, imposing stringent requirements on the algorithm's real-time performance, accuracy, and complexity. Furthermore, there is a notable deficiency in these models' understanding of the spatial positioning of anomalies on highway surfaces. This shortfall becomes particularly evident when confronted with multiple target categories that exhibit low differentiation, where the detection precision still necessitates enhancement. Addressing these challenges, we have developed an intelligent patrol platform, capable of autonomous operation and mountable on highway guardrails. This platform is designed to detect and identify potential safety hazards, such as debris and common types of road defects, as anomalous events on the highway surface. In response to the imperative need for heightened efficiency and precision, we have conceptualized and developed YOLOv5s-L-OTA, an innovative lightweight detection model.