  Under the sea, visible spectrum cameras have limited sensing capacity, being
able to detect objects only in clear water, but in a constrained range.
Considering any sea water condition, sonars are more suitable to support
autonomous underwater vehicles' navigation, even in turbid condition. Despite
that sonar suitability, this type of sensor does not provide high-density
information, such as optical sensors, making the process of object recognition
to be more complex. To deal with that problem, we propose a novel trainable
method to detect and recognize (identify) specific target objects under the sea
with a forward-looking sonar. Our method has a preprocessing step in charge of
strongly reducing the sensor noise and seabed background. To represent the
object, our proposed method uses histogram of orientation gradient (HOG) as
feature extractor. HOG ultimately feed a multi-scale oriented detector combined
with a support vector machine to recognize specific trained objects in a
rotation-invariant way. Performance assessment demonstrated promising results,
favoring the method to be applied in underwater remote sensing.
