## 1 Introduction  
In recent years, target recognition under adverse weather conditions has gained widespread attention due to its practical applications in autonomous driving [1], intelligent surveillance, and traffic management [2]. Meanwhile, dust storms, as a common and challenging environment, exist in a variety of classifications [3]; except during strong and very strong dust storms, people still carry out their normal life activities including but not limited to the use of vehicles in the dust storm weather; therefore, license plate recognition and traffic management under dust storm weather are still greatly affected. Airborne dust particles cause severe image degradation, resulting in blurring, reduced contrast, and partial occlusion of targets. These effects make it increasingly difficult for traditional recognition methods to achieve accurate performance. Developing a robust recognition system that can function effectively under such conditions is critical for ensuring public safety and improving traffic efficiency. Currently, various target recognition algorithms have been developed for object detection and classification in standard environments. Traditional methods rely heavily on manual feature extraction techniques such as SIFT [4] and HOG [5]. While these methods perform adequately under normal conditions, their ability to describe features degrades significantly when faced with blurred or low-contrast images. Moreover, sliding window-based object localization is computationally expensive and struggles to meet real-time requirements. With the advent of deep learning and the fact that convolutional neural networks (CNNs) are characterized by local receptive fields [6] and weight sharing [7], CNNs have become a core technology for modern target recognition. CNN-based methods, especially the R-CNN family [8; 9] (R-CNN, Fast R-CNN, Faster R-CNN, and Libra R-CNN), have made significant advances in detection efficiency and accuracy through the use of Regional Proponent Networks (RPNs) [10]. However, these methods still face some challenges when applied to dust storm weather conditions. The R-CNN family has limited feature extraction capability for low-quality images (e.g., blurred and low-contrast images), which makes it difficult to deal with the case of unclear object contours. First, the presence of sandstorms can lead to image blurring, uneven lighting, and contrast reduction, which can interfere with features extracted from the convolutional layer, resulting in an unstable model training process and slow or even failed convergence. Secondly, in such complex environments, the traditional pooling operation of convolutional neural networks may not be able to adequately preserve the key features, thus leading to a decrease in recognition accuracy. The YOLO (You Only Look Once) family of algorithms has made significant progress in real-time object detection by combining object localization and classification into a single-stage detection network, resulting in extremely high detection speeds. However, although the fast detection mechanism of YOLO improves the speed, lower versions of YOLO (e.g., YOLO V1 [11], YOLO V2 [12], YOLO V3 [13], YOLO V4 [14], and YOLOv7 [15]) still face similar challenges as the R-CNN family in applications in sandstorm environments: image blurring and low contrast co-lead to degraded detection accuracy, especially when dealing with partially occluded or missing object information. While they are very robust, they struggle to capture detailed features under such conditions. While the advanced YOLO version (YOLOv8 [16]) effectively addresses some of the issues present in the lower versions of YOLO, the multi-scale detection used in YOLOv8 requires feature extraction and inference at multiple scales, which increases the computational cost and the post-processing complexity, and may lead to slower inference, especially when dealing with high-resolution images.