Unsupervised underwater fish detection fusing flow and objectiveness

Abstract

Scientists today face an onerous task to manually annotate vast amount of underwater video data for fish stock assessment. In this paper, we propose a robust and unsupervised deep learning algorithm to automatically detect fish and thereby easing the burden of manual annotation. The algorithm automates fish sampling in the training stage by fusion of optical flow segments and objective proposals. We auto-generate large amounts of fish samples from the detection of flow motion and based on the flow-objectiveness overlap probability we annotate the true-false samples. We also adapt a biased training weight towards negative samples to reduce noise. In detection, in addition to fused regions, we used a Modified Non-Maximum Suppression (MNMS) algorithm to reduce false classifications on part of the fishes from the aggressive NMS approach. We exhaustively tested our algorithms using NOAA provided, luminance-only underwater fish videos. Our tests have shown that Average Precision (AP) of detection improved by about 10% compared to non-fusion approach and about another 10% by using MNMS.