Safety and efficiency are the most important factors in handling container cranes at ports all over the world. Rapid economic growth has led to a large increase of quay cranes in operation over the past decades, which is consequently paired with an increasing number of crane incidents. Crane operation becomes even more difficult with larger sized cranes, as the safety of these operations are solely depending on the experience of the operator. Thus, this heightens the demand for additional safety assistance devices. In this project, a camera based image processing design is introduced. By detecting the container that is being handled and adjacent ones at high speed, this system can predict and send a warning for a potential collision before the operator actually realizes the risk.
The proposed Edge Approaching Detection algorithm incorporated with the Hue, Saturation, and Value (HSV) algorithm is the key to this design. The combination of these two algorithms make it much faster to detect color-based objects at high speed and in real-time. By taking advantage of HSV’s high efficiency, the computation required by traditional object detection is reduced dramatically. In this paper, this computation will be compared in terms of frames per second (FPS). As a result, accuracy is improved, speed is increased, and if possible, the switch to a cheaper platform powerful enough to support one specific project will reduce costs.
|Commitee:||Ary, James, Nazari, Masoud|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 55/05M(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Computer science|
|Keywords:||Collision warning, Crane, High-speed, Object detection, OpenCV, Safety|
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