Deep learning with self-supervision and uncertainty regularization to count fish in underwater images

by   Penny Tarling, et al.

Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data.


page 3

page 5

page 7

page 13

page 15


Crowd Counting with Decomposed Uncertainty

Research in neural networks in the field of computer vision has achieved...

Completely Self-Supervised Crowd Counting via Distribution Matching

Dense crowd counting is a challenging task that demands millions of head...

Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision

Object counting is a seemingly simple task with diverse real-world appli...

Indiscernible Object Counting in Underwater Scenes

Recently, indiscernible scene understanding has attracted a lot of atten...

Whale Detection Enhancement through Synthetic Satellite Images

With a number of marine populations in rapid decline, collecting and ana...

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

The success of modern farming and plant breeding relies on accurate and ...

A deep active learning system for species identification and counting in camera trap images

Biodiversity conservation depends on accurate, up-to-date information ab...

Please sign up or login with your details

Forgot password? Click here to reset