# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 3 Data and Methodology
### Methodology  
A prominent representative in object detection algorithms is YOLO, proposed by [PERSON] et al. [13] Operating on an end-to-end basis, it delineates and classifies objects in the original image, demonstrating effective recognition capabilities, albeit with some limitations in accuracy. To enhance accuracy, [PERSON] et al. [14] introduced YOLOv3, employing Feature Pyramid Networks (FPN) to augment detection performance, notably in small object scenarios. However, YOLOv3's efficacy in detecting objects with complex features was suboptimal. [PERSON] et al. [15] conducted multiple optimizations on the YOLO series, leading to the proposal of YOLOv4, which achieved enhanced performance metrics in applications. [PERSON] et al. [16] built upon this with YOLOv5, introducing modifications such as a Focus structure and adaptive anchor box calculations, resulting in a more streamlined architecture and enhanced accuracy. The YOLOv7, proposed by [PERSON] et al. [17], further enhances and refines this approach, introducing the E-ELAN module to bolster network learning capabilities. The YOLOv8, specifically the YOLOv8-Nano variant, proposed by [PERSON] et al. [18], adopting anchor-free technology, addressed the limitations of anchor box-based models, simplifying computational demands for lightweight applications. Finally, the YOLOX, with a focus on the YOLOX-Nano model proposed by [PERSON] et al. [19], combines various strengths of the YOLO series. It innovatively incorporates a decoupled head for faster convergence and higher precision, alongside anchor-free methods and SimOTA dynamic positive sample matching, achieving high-precision, rapid object detection and recognition in compact, resource-constrained environments.  
In conclusion, to effectively detect mudslide geological disasters in remote sensing imagery and enhance target detection capabilities, the study adopted the following research methodologies:  
* Utilizing the meticulously prepared dataset, comparative analyses of the YOLO models in their varying sizes--nano, small, medium, large, and X-large--were conducted under identical environmental conditions. This comprehensive comparison led to the conclusion that the YOLOX-Nano model, among the variants assessed, demonstrated the most superior performance for our specific application in detecting mudslide geological disasters. Consequently, YOLOX-Nano, being a lightweight derivative of YOLOX, was chosen as the fundamental model for the accurate detection of mudslide geological disasters in expansive regions.
* Attention mechanisms were integrated into the Focus, SPP, and PAFPN modules of the YOLOX-Nano network, thereby augmenting the accuracy of mudslide target detection and increasing the network's sensitivity to smaller targets.
* The research incorporated an advanced regression loss function, known as \(eloU\), in lieu of the traditional \(IoU\) function within the base model. This modification was aimed at intensifying the regression accuracy of predictive bounding boxes for smaller targets, consequently leading to an improvement in the model's overall detection capabilities.  
Figure 2: The debris flow JPG sample on the (**left**) is the labeled debris flow sample in the (**middle**) and the generated data set on the (**right**).  
Data augmentation was accomplished using _Mosaic_ and _Mixup_ techniques, thereby enhancing the model's capacity for generalization.