# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 1 Introduction  
With the rapid development of highway networks, the construction of mountain highways has become an important way to connect cities and villages and promote economic development. However, complex geological environments, special climatic conditions, and human factors in mountainous areas have led to the frequent occurrence of rockfall disasters, which bring great threats to driving safety. Especially in steep slope areas, rockfall disasters are unpredictable and sudden, and once they occur, they may cause serious traffic accidents and loss of life and property and even lead to traffic disruption. In 2013, in the State of Colorado, United States, several huge rocks accidentally tumbled down a cliff 4267 m above sea level, some of which weighed more than 100 tons and were the size of a car, causing injuries and deaths. In Taroko National Park, it is estimated that about TWD 20 billion in tourism revenue is lost annually due to fewer tourists as a result of frequent rockfalls. Therefore, how to effectively monitor and detect falling rocks in a timely manner has become an important research topic to ensure highway safety [1]. Existing slope monitoring and rockfall detection techniques mainly rely on traditional sensors, such as geo-radar [2], accelerometers [3], and inclinometers [4], and aero-photogrammetry, which leverages UAVs with high-resolution cameras, has become a powerful method for monitoring rockfalls on steep slopes. This technique allows for the detailed mapping of terrain and rock movement, providing critical data for assessing rockfall susceptibility [5]. By capturing precise 3D landscape models, aero-photogrammetry enhances understanding of rockfall dynamics, essential for evaluating the risk in challenging environments. However, these devices are often constrained by high installation costs, complex maintenance, and limited coverage. In addition, complex terrain and environmental conditions can increase the monitoring difficulty and affect the detection accuracy. In recent years, image-based automatic detection technology has developed rapidly, especially driven by deep learning, and target detection technology has made significant progress. In the field of target detection, the YOLO (You Only Look Once) algorithm [6] has become an important tool for solving various types of target detection problems due to its good detection accuracy and real-time performance. The YOLO series of algorithms greatly improves detection efficiency through the single-phase detection method, which is especially suitable for application scenarios such as highway rockfalls, which require real-time detection and response. With the continuous development of transportation infrastructure construction, the monitoring and early warning of slope rockfall disasters has become a key research direction in transportation safety management worldwide. In recent years, scholars at home and abroad have conducted a large number of studies on slope rockfall monitoring and have made significant progress with respect to different technical paths and means. The existing research can be broadly categorized into monitoring technology based on traditional physical sensors [7] and intelligent detection technology based on computer vision and deep learning [8]. With the development of computer vision technology, image-based intelligent detection technology has gradually become one of the mainstream directions for slope rockfall monitoring. Compared with the traditional physical sensor method, computer vision technology realizes target detection through the use of cameras and deep learning algorithms, which can realize real-time monitoring in a wide range of complex environments and are not limited by physical contact. Early image processing methods such as edge detection and background subtraction [9], were initially applied in rockfall detection, but the performance of these methods was not stable enough due to their sensitivity to ambient lighting and background complexity.