Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Pest Animal's Detection and Habitat Identification in Low-resolution Airborne Thermal Imagery

Version 1 : Received: 19 September 2020 / Approved: 20 September 2020 / Online: 20 September 2020 (14:53:47 CEST)
Version 2 : Received: 20 September 2020 / Approved: 21 September 2020 / Online: 21 September 2020 (06:01:38 CEST)

How to cite: Ulhaq, A.; Khan, A. Pest Animal's Detection and Habitat Identification in Low-resolution Airborne Thermal Imagery. Preprints 2020, 2020090480 (doi: 10.20944/preprints202009.0480.v2). Ulhaq, A.; Khan, A. Pest Animal's Detection and Habitat Identification in Low-resolution Airborne Thermal Imagery. Preprints 2020, 2020090480 (doi: 10.20944/preprints202009.0480.v2).

Abstract

Invasive species are significant threats to global agriculture and food security being the major causes of crop loss. An operative biosecurity policy requires full automation of detection and habitat identification of the potential pests and pathogens. Unmanned Aerial Vehicles (UAVs) mounted thermal imaging cameras can observe and detect pest animals and their habitats, and estimate their population size around the clock. However, their effectiveness becomes limited due to manual detection of cryptic species in hours of captured flight videos, failure in habitat disclosure and the requirement of expensive high-resolution cameras. Therefore, the cost and efficiency trade-off often restricts the use of these systems. In this paper, we present an invasive animal species detection system that uses cost-effectiveness of consumer-level cameras while harnessing the power of transfer learning and an optimised small object detection algorithm. Our proposed optimised object detection algorithm named Optimised YOLO (OYOLO) enhances YOLO (You Only Look Once) by improving its training and structure for remote detection of elusive targets. Our system, trained on the massive data collected from New South Wales and Western Australia, can detect invasive species (rabbits, Kangaroos and pigs) in real-time with a higher probability of detection (85–100 %), compared to the manual detection. This work will enhance the visual analysis of pest species while performing well on low, medium and high-resolution thermal imagery, and equally accessible to all stakeholders and end-users in Australia via a public cloud.

Subject Areas

invasive species; thermal imaging; habitat identification; deep learning

Comments (1)

Comment 1
Received: 21 September 2020
Commenter: Asim Khan
Commenter's Conflict of Interests: Author
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