DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO

2025-03-06

AI Summary

To address small-object detection challenges in drone imagery—namely, missed detections, localization instability, and computational redundancy—this paper proposes a lightweight and efficient small-object detection framework. Methodologically: (1) a joint center-sensitive loss function combining WIoU and NWD is designed to enhance small-object localization accuracy; (2) linear deformable convolution and a Scale-Sequence Feature Fusion (SSFF) module are introduced to strengthen multi-scale feature representation; and (3) a two-stage inference paradigm is adopted to significantly improve localization robustness for low-confidence detections. Built upon YOLOv8-N, the proposed model reduces parameter count by 69.5% while achieving substantial mAP gains on wildlife monitoring benchmarks. It thus delivers high detection accuracy, low inference latency, and suitability for edge-device deployment.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Although advances in deep learning and aerial surveillance technology are improving wildlife conservation efforts, complex and erratic environmental conditions still pose a problem, requiring innovative solutions for cost-effective small animal detection. This work introduces DEAL-YOLO, a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images by using multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box, ensuring smoother localization and reducing abrupt deviations. Additionally, the model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency. The Scaled Sequence Feature Fusion (SSFF) module enhances object detection by effectively capturing inter-scale relationships, improving feature representation, and boosting metrics through optimized multiscale fusion. Comparison with baseline models reveals high efficacy with up to 69.5% fewer parameters compared to vanilla Yolov8-N, highlighting the robustness of the proposed modifications. Through this approach, our paper aims to facilitate the detection of endangered species, animal population analysis, habitat monitoring, biodiversity research, and various other applications that enrich wildlife conservation efforts. DEAL-YOLO employs a two-stage inference paradigm for object detection, refining selected regions to improve localization and confidence. This approach enhances performance, especially for small instances with low objectness scores.

Problem

• Improves small object detection in UAV images.
• Optimizes model for computational efficiency and accuracy.
• Facilitates wildlife conservation through enhanced animal localization.

Innovation

• Uses WIoU and NWD for precise object localization
• Implements LD convolutions for efficient feature extraction
• Incorporates SSFF for enhanced multiscale feature fusion