Road Construction Area Prohibition Sign Recognition Image Dataset

As urbanization progresses, road construction is increasing frequently. Efficiently recognizing prohibition signs in construction areas to ensure driving and construction safety is a major challenge in the current safety protection field. Existing manual monitoring and traditional video surveillance schemes have significant shortcomings in human resource consumption and response speed, making it difficult to achieve real-time and efficient sign recognition and warning applications. This dataset focuses on improving the image recognition system's ability to automatically recognize construction signs, thereby increasing the safety and efficiency of road operations. Images in the dataset are collected by high-resolution cameras installed on vehicle devices and roadside cameras under various weather and lighting conditions. Quality control is strict, with multiple rounds of annotation and consistency checks to ensure all labeled data is reviewed by experts, operated by a professional team with experience in safety sign recognition, consisting of more than 20 people. Data preprocessing includes image denoising, size standardization, data augmentation, and other steps, stored in JPG format for efficient access and processing. Images are orderly classified and labeled. The core advantage of this dataset is its high-quality annotation, with annotation accuracy exceeding 95% and annotation consistency superior by 10% compared to similar datasets. Innovative image enhancement techniques and sign detection algorithms are employed to enhance the dataset's application performance, solving issues of missed detection and false positives in construction area sign recognition, improving performance metrics by 20% compared to traditional methods. Compared to other datasets, this dataset covers more construction scenarios and weather conditions, offering better data diversity. Its structured organization and standardized processing also provide convenience for extended applications, demonstrating excellent versatility and adaptability.