Abstract
Bulldozers, pivotal in earthworks, traditionally undergo supervision through labor-intensive and potentially unreliable manual methods. This research proposes a vision-based method for automating the monitoring of bulldozer operations. First, this research develops a specialized dataset for deep learning, the bulldozer earthmoving activity dataset. Following this, a novel multi-task video classification network (MTVTNet), the multi-task video transformer network, utilizing a video swin transformer architecture, is proposed. This network is adept at concurrently detecting the shoveling action, state, and soil classification of a bulldozer. The effectiveness of this model is demonstrated through its application in a real-world construction setting, achieving a remarkable 99.68% mean average precision. This method not only facilitates comprehensive automated supervision of bulldozer earthmoving activities but also serves as a valuable data source for assessing the operational efficiency of these machines.
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