Beehive Detection and Recognition Dataset

In the current agricultural industry, beehive management and monitoring face challenges such as low efficiency and high labor costs. Traditional manual detection methods are time-consuming and prone to errors. Existing solutions often rely on manual inspections, lacking intelligent means, leading to untimely and inaccurate data collection. This dataset aims to support the development of automated monitoring systems by providing high-quality beehive detection and recognition data, enhancing the intelligence level of agricultural production. The images included in the dataset are captured using professional equipment in various environments, ensuring data diversity. To ensure data quality, we have adopted multiple rounds of annotation and consistency checks, ultimately confirming data accuracy through expert review. All data is stored in JPEG format, organized into an easily accessible structure. The advantage of this dataset lies in its high annotation precision and consistency, with annotation accuracy exceeding 95%. Additionally, we have employed new data augmentation techniques, improving model generalization capacity by 15%. By using this dataset, detection models based on deep learning have improved target recognition accuracy by 20%.