Quantitative performance analysis of object detection algorithms on underwater video footage

Object detection in underwater unconstrained environments is useful in domains like marine biology and geology, where the scientists need to study fish populations, underwater geological events etc. However, in literature, very little can be found regarding fish detection in unconstrained underwater videos. Nevertheless, the unconstrained underwater video domain constitutes a perfect soil for bringing state-of-the-art object detection algorithms to their limits because of the nature of the scenes, which often present with a number of intrinsic difficulties (e.g. multi-modal backgrounds, complex textures and color patterns, ever-changing illumination etc..). In this paper, we evaluated the performance of six state-of-the-art object detection algorithms in the task of fish detection in unconstrained, underwater video footage, discussing the properties of each of them and giving a detailed report of the achieved performance.