# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 1 Introduction  
The detection of objects in foggy conditions plays a crucial role in the field of autonomous driving [1]. However, the complex and unpredictable traffic scenarios demand high stability and accuracy from autonomous detection systems. Issues such as image blurring, reduced contrast, and color distortion caused by fog significantly impair the recognition tasks of detection systems, thereby increasing the risk of traffic accidents. Therefore, research into foggy weather object detection holds significant practical value and societal importance.  
In recent years, researchers have proposed various methods to address the problem of object detection under adverse weather conditions. Some studies have focused on directly applying object detection algorithms to foggy images, such as using the YOLO series [2; 3; 4; 5] and RCNN series [6]. Although it is possible to attempt training or fine-tuning of a detector directly in foggy conditions, the low contrast, blurred details, and color distortion of foggy images make feature extraction challenging. As a result, the model may struggle to effectively learn target features in foggy conditions. This discrepancy in features can impact detection performance, and existing detectors are typically trained on clear images, making them less adaptable to foggy environments. Therefore, training a model directly in foggy conditions may not be as effective as dehazing the images first and then performing detection. One of the strategies for preprocessing images before detection is using defogging algorithms [7; 8]. Defogging algorithms can enhance image contrastand clarity, thereby improving the accuracy of object detection, for example, by restoring image clarity through physical model-based methods (such as dark channel prior) and deep learning methods. This paper proposes the AODs-CLYOLO model based on the idea of defogging before detection.  
AODs-CLYOLO combines the dehazing model AODs with the object detection model CLYOLO. Firstly, AODNet [7] is enhanced for better compatibility with object detection, resulting in AODs. AODs integrates a new parameter estimation module, dehazing module, and introduces a novel contrast regularization in its loss function. Secondly, we develop CLYOLO based on the YOLOv5 framework, incorporating the learnable Cross-Stage Partial Connection Module (CSPCM++) and LSKNet selection attention mechanism in its detection head. Finally, AODs and CLYOLO are merged to create AODs-CLYOLO, a dehazed object detection model (Figure 1).  
The main contributions of this paper are summarized as follows:  
* Introduced a new dehazing model, AODs, which incorporates a deeper multiscale feature fusion structure and a novel loss function with Contrastive Regularization. This reduces the loss of image details during dehazing operations, making the output images more suitable for detection tasks.
* Designed a novel Learnable Cross-Stage Partial Connection Module (CSPCM++), utilizing an improved learnable ConvMixer module and enhanced gradient combinations. This significantly improves metrics with minimal increase in model parameters.
* Proposed a new object detection network, CLYOLO, which takes AODs-processed images as input. CLYOLO integrates the LSKNet selection attention mechanism, depth-wise separable convolutions, and CSPCM++ detection head.
* Constructed a synthetic foggy weather dataset composed of clear images from the commonly used VOC dataset [9], and foggy images generated by randomly adding atmospheric scattering components based on models.