# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 6 Conclusions  
This study focuses on the application of detecting debris flow geological disasters in remote sensing imagery. Compared to manual visual interpretation methods, utilizing computer vision for detecting debris flow geological disasters offers advantages such as lower cost, higher accuracy, and reduced latency. This paper introduces attention mechanisms to enhance the Focus, SPP, and FPN modules of the YOLOX-Nano classification model, and integrates Mosaic and Mix-up data augmentation strategies. By replacing the \(IoU\) loss function with the \(EIoU\) loss function, an improved YOLOX-Nano optimized network is proposed. Utilizing this network for training and testing on a debris flow disaster remote sensing image dataset, and deploying it on a platform for intelligent detection of feature factors in remote sensing images enhances the level of intelligence in the field of remote sensing geological interpretation. The conclusions are as follows:  
Adopting YOLOX-Nano as the foundational network, and enhancing its Focus, SPP, and PAFPN modules with various attention mechanisms, has led to more effective extraction of debris flow geological disaster features. This approach not only maintains a low parameter count but also boosts the model's recognition and classification accuracy, enhancing the \(mAP\) for debris flow geological disaster detection by 3.53% compared to the original model.  
Various attention mechanisms facilitate the redistribution of weights across different feature map channels, augmenting the extraction of deep structural information and fine-grained features. Moreover, the combination of Mosaic and Mix-up data augmentation strategies enhances smaller network performance, and the \(EIoU\) loss function more effectively controls the boundaries of detection boxes, thereby improving the detection accuracy of debris flow geological disasters in remote sensing images.  
Comparative analyses with multiple models, under identical training conditions, reveal that the improved RS-YOLOX-Nano model, with a parameter size of just 3.83 MB, offers substantial advantages in terms of computational efficiency and recognition accu  
Figure 12: Comparative diagram illustrating the actual detection effects before and after the algorithm’s improvement.  
racy. This refined model significantly reduces the computational demands on deployment platforms, providing robust technical support for the intelligent recognition of debris flow geological disasters.  
Conceptualization, S.M. and J.W.; methodology, S.M.; software, J.W.; validation, Z.Z. and Y.T.; formal analysis, S.M.; investigation, J.W.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, S.M.; writing--review and editing, Y.T.; visualization, S.M.; supervision, Y.T.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Geological Survey Project (grant No. DD20191016, ZD20220409).  
The data are available from the corresponding authors on reasonable request.  
The authors would like to thank each member of the team for their efforts.  
The authors declare no conflicts of interest.