RESUMO
An underwater imaging system was investigated for automotive use in highly scattered underwater environments. The purpose of the system is the driver's information about hidden obstacles, such as stones, driftwood, open sewer hatches. A comparison of various underwater vision methods was presented by the way they are implemented, the range reached, and the cost of implementation. It has been experimentally shown that a conventional active system can provide a maximum visibility range of up to three light attenuation lengths. In most practical cases of turbid waters during floods, this corresponds to distances of about 1 meter. From the presented analysis it follows that advanced extended range imaging methods allow increasing of the visibility range up to 2 meters.
RESUMO
In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.
