# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
###### Abstract  
Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios. multi-scale adaptive YOLO; obstacle detection; rail transit; foggy weather; deep learning +
Footnote †: journal: Medical Imaging  
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a single-shot multibox detector [10]. These methodologies facilitate end-to-end object detection, obviating the necessity for manual feature engineering, thereby yielding substantial enhancements in both precision and computational efficiency. They demonstrate heightened discriminative capabilities and improved generalization performance. In the realm of object detection, YOLO epitomizes single-stage algorithms that harness convolutional neural networks to extract target features, thereby transforming the classification quandary into a regression one. This paradigm shift results in the direct provision of insights regarding target categories and their respective positional domains, thus markedly amplifying detection efficacy. [PERSON] proposed the FE-YOLO algorithm, an improved CNN-based one-stage object detection method, to enhance obstacle detection accuracy in rail transit environments, focusing on small and irregular obstacles [11]. [PERSON] proposed the SEF-Net algorithm, which enhances railway obstacle detection by improving accuracy and speed, particularly for small objects in complex environments. It integrates stable bottom feature extraction, lightweight feature extraction, and adaptive feature fusion modules [12]. The algorithm proposed above effectively addresses obstacle detection in rail transit under clear or light foggy weather conditions. However, detecting obstacles during severe foggy weather remains a significant unresolved challenge in rail transit safety. Compared to obstacle detection in clear weather conditions, research on detecting obstacles in dense, foggy weather is relatively limited. Utilizing defog models for image enhancement has emerged as a powerful technique and has seen significant advancements in recent years. [PERSON] proposed a method to remove haze from a single image using the dark channel, which relies on the observation that haze-free outdoor images have pixels with very low intensities in at least one color channel [13]. [PERSON] introduced an unsupervised domain adaptive object detection framework designed for adverse weather conditions like haze and rain, using weather-specific priors to improve detection performance by minimizing weather-related distortions in image features [14]. However, image degradation caused by dense fog significantly lowers detection accuracy, resulting in issues such as missed detections and false alarms. A target detection model trained on high-quality, clear images often fails to achieve satisfactory performance under adverse weather conditions, such as dense fog. One effective approach is to decompose images captured in these adverse conditions into clean images and their corresponding weather information. Based on weather information, image quality can be appropriately enhanced, potentially recovering more latent information about originally blurred and misidentified objects.