Are object detection assessment criteria ready for maritime computer vision?
This work tackles the challenge of adapting assessment criteria for object detection in maritime environments, which is incremental as it modifies existing metrics rather than introducing a new paradigm.
The paper addresses the problem of conventional object detection metrics being inadequate for maritime computer vision, proposing new bottom edge proximity metrics to assess detection quality, which show that existing computer vision methods are promising for this domain.
Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision.