Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys

• URL: http://arxiv.org/abs/2505.10737v1
• Date: Thu, 15 May 2025 22:42:44 GMT
• Title: Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
• Authors: Mitchell Rogers, Theo Thompson, Isla Duporge, Johannes Fischer, Klemens Pütz, Thomas Mattern, Bing Xue, Mengjie Zhang,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) provide a cost-effective means of capturing high-resolution imagery.<n>We assess the performance of a general-purpose avian detection model, BirdDetector, in estimating the breeding population of Salvin's albatross (Thalassarche salvini) on the Bounty Islands, New Zealand.
• Score: 4.936287307711449
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in deep learning and aerial imaging have transformed wildlife monitoring, enabling researchers to survey wildlife populations at unprecedented scales. Unmanned Aerial Vehicles (UAVs) provide a cost-effective means of capturing high-resolution imagery, particularly for monitoring densely populated seabird colonies. In this study, we assess the performance of a general-purpose avian detection model, BirdDetector, in estimating the breeding population of Salvin's albatross (Thalassarche salvini) on the Bounty Islands, New Zealand. Using drone-derived imagery, we evaluate the model's effectiveness in both zero-shot and fine-tuned settings, incorporating enhanced inference techniques and stronger augmentation methods. Our findings indicate that while applying the model in a zero-shot setting offers a strong baseline, fine-tuning with annotations from the target domain and stronger image augmentation leads to marked improvements in detection accuracy. These results highlight the potential of leveraging pre-trained deep-learning models for species-specific monitoring in remote and challenging environments.

Related papers

• Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision [46.87579355047397]
  This paper proposes a novel method that uses generative AI to synthesize high-quality aerial images and their labels.<n>Our key contribution is the development of a multi-stage, multi-modal knowledge transfer framework.
  arXiv  Detail & Related papers  (2025-07-28T16:38:06Z)
• Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches [1.130790932059036]
  This study compares the performance of state-of-the-art neural networks for detecting marsh deer in UAV imagery.<n>We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included.
  arXiv  Detail & Related papers  (2025-05-30T18:45:42Z)
• UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting [57.63613048492219]
  We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial vehicles (UAVs)<n>This is achieved by integrating 3D Gaussian Splatting (3DGS) for reconstructing backgrounds along with controllable synthetic human models that display diverse appearances and actions in multiple poses.
  arXiv  Detail & Related papers  (2025-04-02T22:17:30Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images [57.96659470133514]
  Motion-activated camera traps constitute an efficient tool for tracking and monitoring wildlife populations across the globe. Supervised learning techniques have been successfully deployed to analyze such imagery, however training such techniques requires annotations from experts. Reducing the reliance on costly labelled data has immense potential in developing large-scale wildlife tracking solutions with markedly less human labor.
  arXiv  Detail & Related papers  (2023-11-02T08:32:00Z)
• Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation [7.906113472259946]
  The aquaculture sector in New Zealand is experiencing rapid expansion, with a particular emphasis on mussel exports. As the demands of mussel farming operations continue to evolve, the integration of artificial intelligence and computer vision techniques is emerging as an effective approach to enhance operational efficiency. This study delves into advancing buoy detection by leveraging deep learning methodologies for intelligent mussel farm monitoring and management.
  arXiv  Detail & Related papers  (2023-08-18T01:53:47Z)
• Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder [4.812718493682454]
  This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. An anomaly detection framework computes metrics directly on the input space, enhancing interpretability and anomaly localization. Our framework offers improved interpretability and localization of anomalies, providing valuable insights for monitoring marine ecosystems.
  arXiv  Detail & Related papers  (2023-07-13T12:26:27Z)
• Object counting from aerial remote sensing images: application to wildlife and marine mammals [4.812718493682454]
  Anthropogenic activities pose threats to wildlife and marine fauna. This research study utilizes deep learning techniques to automate animal counting tasks. The model accurately locates animals despite complex image background conditions.
  arXiv  Detail & Related papers  (2023-06-17T23:14:53Z)
• Deep object detection for waterbird monitoring using aerial imagery [56.1262568293658]
  In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast.
  arXiv  Detail & Related papers  (2022-10-10T17:37:56Z)
• Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance [3.8015092217142223]
  We show that a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network can increase the detection efficacy in real-world settings. We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED.
  arXiv  Detail & Related papers  (2021-11-12T17:30:55Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.