Perceptions for Crane Operations

Sensors and computation increase the precision, efficiency, and agility of crane operations. This paper presents an ongoing work of developing computational methods to enhance the crane operations. This research focuses on three significant challenges for crane operators: (1) identifying construction equipment and their activities, (2) identifying and tracking personnel, and (3) tracking the rigging object. In the preliminary stage, we focus on the first challenge to identify construction equipment and activities. 5,000 images have been collected and manually labeled for training deep learning detection algorithms. In the future steps, we will employ inception-SSD method to locate personnel, trucks, and excavators. After that, we will propose a method that recognizes excavator activities from crane views. Once learning algorithm is the reliable, it will benefit the crane operators to operate the cranes with confidence. It can also reduce the difficulty of crane operation. Training time and safety concerns will be reduced simultaneously.