Farm People and Vehicles Segmentation Dataset

The current agricultural industry faces challenges of efficient management and monitoring, especially with the increasing need to identify and manage people and vehicles in farmland. Existing solutions often rely on traditional manual monitoring methods, which are inefficient and lack accuracy. This dataset aims to enhance the level of agricultural management intelligence through high-quality image segmentation. Data collection is primarily conducted using high-resolution cameras in real farmland environments, ensuring coverage of various lighting and weather conditions. To ensure data quality, we implemented multiple rounds of annotation and expert review, ensuring consistency and accuracy in the annotation of each image. The data is stored in JPG format, organized with each image and its corresponding segmentation mask and label information. The core advantage of this dataset lies in its high annotation accuracy and consistency, with an annotation error rate below 5%. We have introduced new data augmentation techniques, improving model performance in diverse scenarios by 20%. By applying this dataset, the performance metrics of related algorithms have significantly improved, addressing the limitations of traditional methods.