# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 1 Introduction
With the development of deep learning technology, the target detection model based on a convolutional neural network (CNN) [10] has become the mainstream research direction for slope rockfall detection. [PERSON], [PERSON], et al. [11] used the Faster R-CNN model for real-time monitoring of mountainous slopes, and using a large-scale image dataset for training, the system achieved satisfactory landslide detection performance, with no need to retrain the model with new samples. [PERSON] et al. proposed a rock roll impact detection algorithm to objectively extract different perceived surface roughnesses from detailed terrain samples by combining it with a rebound model to perform rockfall simulations directly on a detailed 3D point cloud, to extract the perceived surface roughness related to the size of the impacting rock from detailed terrain samples, and to analyze the rockfall from multiple perspectives at the physical level [12]. To solve the problem of uncertainty in location and time due to different sizes of falling rocks, [PERSON] et al. used the 8987 3D point cloud dataset, where rockfalls occur frequently, to provide more scientific statistics on the timing and location patterns of rock roll [13]. [PERSON] et al. analyzed scientific production by using bibliometrictechniques to study the rolling of falling rock trajectories to analyze and reason about the falling trends of falling rocks and provided a systematic assessment of the risk of rockfall hazards through the use of large-sample statistics [14]. Research on rockfall detection based on computer vision has also made rapid progress. [PERSON] [15] and others proposed a YOLOX video streaming-based rockfall detection model for slopes, aiming to solve the problems of missed detection, false detection, and trajectory interruption that occur in online deep learning multi-target tracking. The detection phase uses a three-frame differencing method combined with an offline-trained YOLOX model for rock detection, while the tracking phase re-matches the unmatched trajectories based on a combination of data correlation and moving object detection results. In addition, [PERSON], [PERSON], et al. [16] achieved competitive results for dilemmas such as falling rocks on roadways based on YOLOv5 network optimization with fewer parameters, as well as simpler training, and efficiently identified scattered falling rocks by leveraging the feature extraction capability of YOLOv5. Meanwhile, [PERSON] et al. [17] proposed an integrated system based on YOLOv8 detection technology, which focuses on a variety of terrain safety issues, including debris landslides, while the system's multi-threaded controller ensures concurrent execution in order to analyze the video source in real time so as to quickly respond to potential hazards and provide real-time alarms. Research based on the latest target detection algorithms, such as YOLOv8, has begun to gradually enter the field. YOLOv8 has stronger feature extraction capabilities and higher detection accuracy than its predecessor, especially in multi-scale target detection. Currently, related studies at home and abroad are gradually exploring how to combine the advantages of YOLOv8 to further improve the accuracy and robustness of slope rockfall detection. For example, certain researches have enhanced the model's ability to recognize small targets by introducing an attention mechanism or multi-scale feature fusion module while, at the same time, simulating complex rockfall scenarios by means of data enhancement and other means to improve the model's generalization ability. Therefore, we choose to optimize the model using the YOLOv8 framework to further improve the algorithm's accuracy and feature recognition ability in complex environments. This paper proposes an effective method for falling rock detection. This method can distinguish falling rocks from other objects and detect them in real time in complex environments. The main contributions outlined are as follows:  
1.