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.

[1]  Jean-Philippe Tarel,et al.  Fast visibility restoration from a single color or gray level image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Kazushi Nakano,et al.  Nominal performance recovery by PID+Q controller and its application to antisway control of crane lifter with visual feedback , 2004, IEEE Transactions on Control Systems Technology.

[3]  M. Wilscy,et al.  A Novel Wavelet Fusion Method for Contrast Correction and Visibility Enhancement of Color Images , 2022 .

[4]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

[5]  Frédo Durand,et al.  Motion-invariant photography , 2008, SIGGRAPH 2008.

[6]  Shun'ichi Kaneko,et al.  Robust image registration by increment sign correlation , 2002, Pattern Recognit..

[7]  Hae Yong Kim,et al.  Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast , 2007, PSIVT.

[8]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[9]  Hisashi Osumi,et al.  Positioning of wire suspension system using CCD cameras , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Yoav Y. Schechner,et al.  Blind Haze Separation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Michel Dhome,et al.  Real Time Robust Template Matching , 2002, BMVC.

[12]  Y. Choi,et al.  Measurement system design for sway motion based on image sensor , 2009, 2009 International Conference on Networking, Sensing and Control.

[13]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Tsong-Yi Chen,et al.  Visibility Enhancement in the Foggy Environment Based on Color Analysis , 2009, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC).

[15]  T. Takahashi,et al.  Recognition of foggy conditions by in-vehicle camera and millimeter wave radar , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[16]  Miki Haseyama,et al.  A Kalman filter based restoration method for in-vehicle camera images in foggy conditions , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[18]  Adrian Kaehler,et al.  Learning opencv, 1st edition , 2008 .

[19]  Guodong Guo,et al.  Patch-based Image Correlation with Rapid Filtering , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  William Singhose,et al.  Effects of hoisting on the input shaping control of gantry cranes , 2000 .

[21]  Jean-Philippe Tarel,et al.  Automatic fog detection and estimation of visibility distance through use of an onboard camera , 2006, Machine Vision and Applications.

[22]  Jizhou Sun,et al.  Local albedo-insensitive single image dehazing , 2010, The Visual Computer.

[23]  Jung-Jae Lee,et al.  Measurement of 3D Spreader Position Information using the CCD Cameras and a Laser Distance Measuring Unit , 2004 .

[24]  Shun'ichi Kaneko,et al.  Using orientation codes for rotation-invariant template matching , 2004, Pattern Recognit..

[25]  Hajime Asama,et al.  Development of Crane Vision for Positioning Container , 2003 .

[26]  Hideki Kawai,et al.  Anti-sway system with image sensor for container cranes , 2009 .

[27]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Y. Yoshida,et al.  Visual tracking and control of a moving overhead crane load , 2006, 9th IEEE International Workshop on Advanced Motion Control, 2006..

[29]  John P. Oakley,et al.  Improving image quality in poor visibility conditions using a physical model for contrast degradation , 1998, IEEE Trans. Image Process..

[30]  Eui-Young Cha,et al.  Real-time container position estimation method using stereo vision for container auto-landing system , 2010, ICCAS 2010.

[31]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[32]  Masato Kobayashi,et al.  Development of Hoisting Load Position Sensor for Container Handling Cranes , 2001 .

[33]  John E. Tyler,et al.  The nature of light and colour in the open air , 1954 .

[34]  Antonios Gasteratos,et al.  Color-Based Monocular Visuoinertial 3-D Pose Estimation of a Volant Robot , 2010, IEEE Transactions on Instrumentation and Measurement.