Excavation Site Robotic Arm Action Control Dataset

Currently, the construction and real estate industry faces challenges of low efficiency and high risk of manual operations. Existing robotic arm control methods rely on fixed programs and manual intervention, lacking flexibility and intelligence. Against this backdrop, this dataset aims to enhance the automatic control and operational efficiency of robotic arms through machine learning, reducing human operational errors. The dataset primarily uses high-resolution cameras to capture video data of robotic arm operations in real construction site environments, with these cameras installed at different angles to ensure thorough recording. Multiple rounds of annotation and consistency checks ensure data quality, reviewed by senior experts in artificial intelligence and control fields. The annotation team comprises over 20 experienced engineers, ensuring annotation accuracy and consistency. Data preprocessing includes steps such as denoising and video segmentation, with advanced video encoding technologies employed for storage and organization.This dataset is renowned for its high-precision, high-consistency annotation quality, with annotation accuracy exceeding 95%. Innovative dynamic region annotation methods are used, and data augmentation techniques are introduced to improve algorithm robustness and accuracy. It effectively addresses the flexibility issues in construction robotic arm control and significantly improves construction efficiency indicators, increasing work efficiency by approximately 50%. Compared to other similar datasets, this dataset has greater advantages in diversity and coverage, applicable for robotic arm operation training in various construction scenarios. The long-duration recording and multi-angle capture provide significant advantages in scarcity and uniqueness. Furthermore, this dataset is expandable to other heavy machinery automation fields, offering broad general applicability.