Abstract
Imaging is an important means to explore the ocean for underwater robotics. The diffuse attenuation coefficient of light in water is one of the most important optical properties of seawater. This article presents a model-based method to analyze the causes of distortion of underwater images. We built a platform for underwater image acquisition and target recognition. The model coefficients were calibrated with images captured underwater and in air. Experiments were carried out to verify the designed algorithm and the transmission error model. The experiments show that the presented method works well in improving the accuracy of feature extraction and recognition of underwater targets.
Highlights
In recent years, demands for subsea inspection are increasing with a growth in a number of activities in deep ocean.[1]Underwater robotics have been increasingly deployed for numerous missions related to oceanography, hydrography, coastal, and inland water monitoring.[2]
Task assignment and path planning are the objects of their study.[5]
The experimental results are showed as follows: By studying existing researches, the visibility of the sea water varies between 0.3 m and 27 m, which depends on water properties and light
Summary
Demands for subsea inspection are increasing with a growth in a number of activities in deep ocean.[1]Underwater robotics have been increasingly deployed for numerous missions related to oceanography, hydrography, coastal, and inland water monitoring.[2]. Using the library of Fourier descriptors and considering the distortion based on the model, the recognition rate is greater than 88% in most experimental groups. Researching on the influence of various factors on losses in transmission of the underwater optical signal, the error model is constructed. It is the recognition accuracy of the underwater target detection algorithm that has important reference significance in underwater image processing.
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