In the world of autonomous underwater vehicles (AUV) the prominent form of sensing has been sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that vision techniques should be completely ignored. There are situations where visibility is high, such is in calm waters, and where light dispersion is not an issue, such as shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this project is this gap in capability for AUVs, with an emphasis on computer-aided detection through machine learning and computer vision techniques. All experimentation utilizes the Stingray AUV, a small and unique vehicle designed by San Diego iBotics. The first experiment is detection of an anchored buoy, which mimics the real world application of mine detection for the Navy. The second experiment is detection of a pipe, which mimics pipes in bays and harbors. The current algorithm for this application uses boosting machine learning on hue, saturation, value (HSV) to create a classifier followed by post processing techniques to clean the resulting binary image. There are many further applications for computer-aided detection and classification of objects underwater, from environmental to military.