## 2 Related Work  
Deep learning has demonstrated remarkable performance in various tasks such as denoising [28], image inpainting, super-resolution [29; 30], deblurring, and style transfer [31]. In the domain of adverse weather restoration, such as defogging, deraining, and desnowing, deep neural networks significantly outperform traditional methods. For instance, in deraining, CNN networks capture features from rain-affected images, enabling the learning of the physical characteristics of raindrops and rain streaks [32; 33]. Concurrently, CNNs learn paired images of rain-free and rain-degraded conditions [34; 35]. However, this method may leave residual blurry regions in images. Other approaches employ GAN (Generative Adversarial Network) networks to eliminate raindrops, but this requires obtaining effective attention maps [36; 37]. Similarly, to address the issue of removing irregular snowflakes from images, GAN networks are used to focus on the features of snowflake patterns [38].  
Before performing object detection tasks in adverse weather scenarios, preprocessing the images is imperative. One direct approach is to remove weather factors from the images and then apply image enhancement techniques before inputting them into the detection network [39; 40; 41; 42]. However, solely employing this method may not achieve detection accuracy comparable to that under normal weather conditions. Another approach relies on unsupervised priors [43], combining image enhancement and detection while learning feature representations of weather to eliminate interference from weather-specific information [44; 45]. For example, [PERSON] et al. [46] proposed a single-image defogging detection framework based on region-line prior [47]. Although improving the quality of adverse weather images is beneficial, it does not necessarily translate to high-precision object detection models [48; 49]. Therefore, some methods connect image processing modules and detection modules end-to-end to address this issue, while others utilize domain adaptation techniques [50; 51].  
To bolster the effectiveness of object detection networks in challenging weather environments, we introduce a new object detection layer known as SwinFocus [52]. This layer enhances both the feature extraction and representation capabilities of YOLOv5, leading to improved detection accuracy, particularly for small and blurry objects in foggy conditions [53]. Additionally, methods have been proposed to optimize the structure of YOLOv5 by reducing the depth of the feature pyramid and limiting the maximum downsampling factor to better recognize small objects [54; 55]. With the emergence of the YOLOv8 detection network, an occlusion-aware attention mechanism has been designed, and variable convolutions have been utilized to enhance the feature extraction capabilities of the YOLOv8 network [56; 57].  
Despite these significant improvements in object detection performance, notable deficiencies remain in parameter tuning due to the lack of focus on feature scales after image processing enhancement and insufficient modifications to object detection models for image processing and enhancement. Therefore, this study introduces an enhanced object detection methodology tailored to unmanned aerial vehicles (UAVs) navigating through inclement weather conditions, referred to as IDP-YOLOV9.  
In this study, we primarily employed a parallel architecture based on image defogging, desnowing, and deraining modules (TRA) and image enhancement modules (DLIE) to process and enhance images captured under various adverse weather conditions at power construction sites by drones. Additionally, we introduced joint training of the improved YOLOV9 module with a three-layer routing attention mechanism to capture features of clear images restored by the TRA and DLIE modules. Finally, object detection was performed on images captured under various adverse weather conditions at power construction sites, thereby enhancing detection accuracy under complex adverse weather conditions.