Measurement of a Container Crane Spreader Under Bad Weather Conditions by Image Restoration

Recently, anti-sway systems for container crane spreaders have played an important role in efficiently handling cargo at terminals. A camera-based measurement system for the anti-sway systems, which consists of two reference marks on the upper surface of the spreader and a camera mounted on the crane´s trolley, has been developed. However, the quality of the reference marks in images taken in bad weather such as mist, fog, and rain is degraded, and consequently, the measurement accuracy also deteriorates. To preserve measurement accuracy in bad weather, a rapid method for image restoration is needed. We propose a method that restores the quality of reference marks in images by modeling the degradation of the light intensity. The two constants in the model are estimated from the variances and averages of the intensities in two reference mark images extracted from two images taken at different heights of the spreader. The height of the spreader, as measured by the system, is also fed back into the restoration. The measurement accuracy attained by the method under simulated bad weather conditions is compared with the accuracy achieved in normal conditions. It is found that measurement accuracy is preserved, with only small errors of up to 1.39 mm under a swaying motion. The processing time of the method is 0.83 ms on average, which does not affect the measurement period of the system.