# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 4 Discussion  
This research proposed the Urban Road Anomaly Visual Large Language Models (URA-VLMs), marking substantial progress in the real-time monitoring of urban road anomalies. Based on a pre-trained generative AI model, InternVL 2.5 MPO, the URA-VLMs exhibit remarkable accuracy and flexibility in identifying and evaluating different types of road anomalies, achieving a comprehensive detection accuracy rate of 93.20% in classifying road anomalies. Specifically, for instances of road flooding, the URA-VLMs noted a depth error of 33.57%, alongside a Mean Absolute Error of 97.21 mm. Regarding road safety levels, the URA-VLMs achieved approximately 90% accuracy across the categories GREEN (92.42%), light green (88.19%), and YELLOW (96.45%). This performance surpasses that of leading existing models, such as InternVL 2.5 and ResNet 34, highlighting the potential of generative AI models to enhance urban safety management. The URA-VLMs framework facilitates real-time identification and classification of urban road anomalies by leveraging a cloud-based system that integrates CCTV cameras with vision large language models. This innovative approach enables continuous monitoring and analysis of road conditions, delivering immediate results to mobile devices. The capabilities are essential for establishing effective early warning systems. The integration of generative AI marks a significant advancement in road anomaly monitoring, enhancing the system's capacity to promptly detect anomalous events and assist in decision-making processes. In comparison with the other AI models for road anomalies monitoring, the URA-VLMs based on InternVL 2.5 MPO outperformed the fine-tuned ResNet-34 in terms of accuracy across multiple evaluation metrics. Compared to state-of-the-art road anomaly AI models, URA-VLMs have achieved relatively high accuracy. For instance, in the related similar roads anomalies motoring tasks, the YOLOv3 attained a mean Average Precision of 65.65% [56], while the CNN classified four types of pavement cracks, achieving an accuracy of 76% [57]. A proposed Mask R-CNN [58; 59] for pixel-wise segmentation of pavement regions achieving 90% accuracy. The significance of data annotation is critical, particularly in the context of road flood monitoring. Given the scarcity of real depth measurements, data annotation frequently depends on human judgment. To address this challenge, we advocate for a multi-source data evaluation approach that integrates diverse observations to achieve a comprehensive understanding of flood conditions. Establishing consistent standards for calibrating depth estimates using reference objects [41], along with a combination of human and machine assessments, improves reliability [18; 41]. The Urban Road Anomaly VLMs have undergone numerous optimizations in the inference processes, employing multi-step reasoning to enhance both efficiency and accuracy. The VLMs consist of three specialized expert models: the Road Anomalies Detect VLM, the Waterlogging Reference Object Detect VLM, and the Waterlogging Depth Describe VLM. These models employ a Chain of Thought (COT) design with multi-step prompts. This methodology facilitates targeted training on specific datasets, thereby enhancing performance and effectively addressing the challenges associated with mixed data training. Bysystematically adjusting prompts and optimizing the retrieval process, we can achieve a balance between the accuracy and efficiency of the large language models. This ensures that the models not only produce precise outputs but also operate within a reasonable timeframe, making them suitable for real-time applications. It is worth noting that URA-VLMs exhibit relatively poor accuracy in recognizing garbage, which needs further discussion. Constructing a large-scale and content-rich garbage dataset requires substantial resources and incurs significant costs.