# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
###### Abstract  
Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster detection. This study evaluated six object detection models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, conducting experiments on remote sensing image data in the study area. Utilizing transfer learning, mudslide remote sensing images were fed into these six models under identical experimental conditions for training. The experimental results demonstrate that YOLOX-Nano's comprehensive performance surpasses that of the other models. Consequently, this study introduces an enhanced model based on YOLOX-Nano (RS-YOLOX-Nano), aimed at further improving the model's generalization capabilities and detection performance in remote sensing imagery. The enhanced model achieves a mean average precision (\(mAP\)) value of 86.04%, a 3.53% increase over the original model, and boasts a precision rate of 89.61%. Compared to the conventional YOLOX-Nano algorithm, the enhanced model demonstrates superior efficacy in detecting mudflow targets within remote sensing imagery. deep learning methods; debris flow disaster target detection; YOLOX-Nano; transfer learning; remote sensing imagery 2020  
employs image texture and spectral characteristics to facilitate the automatic extraction of mudslide inundation area information, thereby enabling quick, accurate, and efficient target detection [3]. Deep learning, a branch of machine learning, attains meaningful data representation by mining information on multiple levels and automatically extracting all features. Particularly in image object detection, deep learning outperforms manual feature extraction by automatically extracting various features and performing progressive information distillation and purification, resulting in significantly improved outcomes compared to traditional algorithms [4]. Among these, the region-free algorithm, a leading deep learning object detection algorithm, inputs the entire image into the neural network and produces immediate detection results [5; 6]. Notable examples are the YOLO series and the SSD algorithm. The YOLO series, in particular, maintains high detection accuracy and the ability to detect small objects, rapidly completing detections and making it suitable for remote sensing image object detection [7]. Currently, leveraging the YOLO algorithm, numerous research endeavors have successfully achieved high-precision detection of various targets in remote sensing images, including fire smoke [8] and maritime vessels [9]. Considering the aforementioned issues and research, to detect debris flow geological hazard targets in remote sensing imagery rapidly, precisely, efficiently, and intelligently, this study initially conducts comparative experiments on geological hazard target detection models utilizing six algorithms: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX. It then selects the model demonstrating higher accuracy and efficiency as the foundational model for debris flow target detection in large-scale, high-resolution remote sensing imagery. The comprehensive analysis of experimental results reveals that YOLOX discards the traditional anchor frame mechanism in favor of anchor-free technology for target parsing and recognition [10]. This approach offers smaller parameter size, faster floating-point operations, lower latency, and maintains high detection accuracy, rendering it ideal for the precise identification of mudslide geological disasters over extensive areas. Consequently, this paper introduces an enhanced target detection algorithm based on YOLOX-Nano, which boosts detection performance by incorporating an attention mechanism (AM) to heighten the model's focus on geological disaster locations.