A high-speed color-based object detection algorithm for quayside crane operator assistance system
Xiang Gao, Hen‐Geul Yeh, Panadda Marayong · 2017 Annual IEEE International Systems Conference (SysCon) · 2017
Improvement to user interface technology for port crane operators can lead to safer and more ergonomie environments for cargo transport. An accurate and responsive container-handling guidance system can increase productivity and reduces costs. In this work, a vision-based assistive system for quayside crane operator is developed for collision warning. The system applies a new object edge detection algorithm, called Edge Approaching, to achieve faster detection rate in real-time using a stand-alone embedded system that can be easily integrated to an existing crane interface. Experiments are conducted on a scaled testbed to validate the concept. The proposed algorithms significantly increase the detection rate from as compared to the conventional Canny edge detection and Hough transform method, while maintaining a high accuracy rate of 99%.