## 1 Introduction  
In the field of electric power, especially in complex environments or secluded areas, the utilization of unmanned aerial vehicles (UAVs) for surveillance, evidence collection, and external risk assessment is crucial for ensuring the safe operation of electrical facilities [1]. However, the quality of images acquired by UAVs is frequently compromised by adverse weather conditions, resulting in diminished clarity and subsequently reducing the accuracy and efficacy of detection [2]. This limitation significantly hinders the surveillance capacity of UAVs, potentially delaying the identification of risks [3] or problems and posing a threat to the safety of electrical infrastructure. Consequently, enhancing the object detection performance of UAVs under adverse weather conditions is imperative. While mainstream detection algorithms such as Faster R-CNN [4], the YOLO series [5; 6; 7], and CenterNet [8] perform satisfactorily on images captured under normal weather conditions, adverse weather conditions often cause image blurring, insufficient illumination, or the overlapping of weather artifacts with objects [9; 10]. Existing image dehazing methods, which utilize multi-scale feature aggregation networks, have shown inefficiency in processing images [11; 12; 13]. Similarly, rain removal techniques employ window self-attention networks and global residual convolution to produce rain-free images [14; 15], while snow removal approaches use restoration algorithms to eliminate the influence of snowflakes [16; 17]. These methods primarily address individual weather-related issues and are inadequate for dealing with the complexity of real-world adverse weather effects. To enhance object detection, the proper supplementation of image data is required. Some studies have attempted to combine image enhancement with object detection using adaptive enhancement modules composed of context branches and edge branches to improve degraded images [18; 19]. These methods dynamically adjust enhancement settings based on the illumination distribution in the input images [20; 21; 22]. However, these approaches often struggle to adapt to various image types, and the adjustment of enhancement parameters heavily relies on manual intervention, leading to suboptimal results. Building on these foundations, some research has integrated image enhancement with object detection [23; 24], proposing the Adaptive Enhancement Model for Object Detection Network (ARODNet) [25] to improve detection performance under adverse conditions [26; 27]. Nonetheless, these methods do not optimize the structure of the detection network itself, limiting their effectiveness during training. Existing object detection methods struggle with degraded image quality and reduced effectiveness under adverse weather conditions such as rain, snow, and fog. These limitations stem from their inability to adequately address individual weather-related issues and adapt to the complexities of real-world scenarios, resulting in suboptimal detection performance. To address these challenges in the field of electric power and overcome the limitations of current object detection methods, this paper introduces an improved deep learning-based object detection model called IDP-YOLOV9. This algorithm integrates advanced image processing techniques and enhanced parallel architectures, utilizing the parameter estimation of convolutional neural networks, along with an improved YOLOV9 detection network model equipped with a three-layer routing attention mechanism. The objective is to enhance image processing capabilities under complex weather conditions, thereby improving object detection accuracy. The primary contributions of this paper are as follows:  
* We designed a parallel optimization architecture for the image processing module TRA (Three-Weather Removal Algorithm) and the image enhancement module DLIE (Deep Learning-based Image Enhancement).