Abstract—This work aims the design and implementation of an adaptive vision-based sensor for detecting a pipe on underwater scenes in real time. The motivation is focused to future applications of vision servo control in underwater vehicles. The approach employs color and shape image segmentation together with an adjust mechanism that aims continuously in time to reach the best setup of a parameter set of the color image segmentation. The sensor performs very well even in the case of large and rapid changes in the scene illumination. On the basis of many experiments carried out in real scenes and the comparison with similar algorithms in the state-of-the-art field on the same application, the approach gets a better positioning with respect to related results above all in the case of extremely changing and poor luminance conditions. As drawback, the required computation time to achieve optimal values for the first time (auto-tuning phase) may be large; contrary to the adaptive ongoing process, in where the optimization is much more agile.

Index Terms—underwater pipeline detection, vision-based sensor, HSV model, optimal parameter adjustments, changing illumination levels, adaptive sensor

Cite: Mario A. Jordán and Emanuel Trabes, "An Adaptive Vision-based Sensor for Underwater Line Detection Employing Shape and Color Image Segmentation," Jounal of Automation and Control Engineering, Vol. 3, No. 6, pp. 480-486, December, 2015. doi: 10.12720/joace.3.6.480-486