Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance

TL;DR

This paper demonstrates that combining deep super-resolution and altitude data with object detection significantly improves aerial animal detection accuracy in drone imagery, enabling more effective biodiversity monitoring.

Contribution

It introduces a novel integration of super-resolution and altitude data into detection pipelines, enhancing performance on aerial animal datasets.

Findings

Super-resolution and altitude data improve detection accuracy.

The approach outperforms state-of-the-art methods.

Detection efficacy increases with animal resolution.

Abstract

Visuals captured by high-flying aerial drones are increasingly used to assess biodiversity and animal population dynamics around the globe. Yet, challenging acquisition scenarios and tiny animal depictions in airborne imagery, despite ultra-high resolution cameras, have so far been limiting factors for applying computer vision detectors successfully with high confidence. In this paper, we address the problem for the first time by combining deep object detectors with super-resolution techniques and altitude data. In particular, we show that the integration of a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network into standard recognition pipelines can considerably increase the detection efficacy in real-world settings. We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED. We find that the proposed approach can consistently improve over ablated baselines and the state-of-the-art performance for both datasets. In addition, we provide a systematic analysis of the relationship between animal resolution and detection performance. We conclude that super-resolution and altitude knowledge exploitation techniques can significantly increase benchmarks across settings and, thus, should be used routinely when detecting minutely resolved animals in aerial imagery.