Object detection and classification for small objects in/on water

Abstract

The ability to quickly detect and identify objects floating on the surface of the water can benefit numerous fields including environmental monitoring, civil infrastructure, and security applications. To address this need, we investigated the ability of multiple imaging sensors to perform machine learning-based object detection and classification for small objects on water. The sensors tested in this effort include a long-wave infrared polarimeter camera, a visible wavelength optical camera, and a low-light camera. Small waste objects such as trash bags, Styrofoam cups, wood, plastic bottles, cardboard, and aluminum cans were placed into a riverine environment on the water surface and data were gathered with each of the sensors. Artificial neural networks were trained based on the data gathered, and models were created to perform object detection and classification of those same objects in the riverine environment. The three-camera systems performed well but with clear advantages for specific environmental conditions and object types.