ArticleAnimals : an open access journal from MDPI2025
YOLO-SDD: An Effective Single-Class Detection Method for Dense Livestock Production.
Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Automated Detection of Quality Deviations in Poultry Processing Using Step-Specific YOLOv12 Models.Foods (Basel, Switzerland) · 2026Article
- Article
- CF-DETR: A Lightweight Real-Time Model for Chicken Face Detection in High-Density Poultry Farming.Animals : an open access journal from MDPI · 2025Article
- A Deep Learning Framework for Multi-Object Tracking in Space Animal Behavior Studies.Animals : an open access journal from MDPI · 2025Article
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6 authors.
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Abstract
Single-class object detection, which focuses on identifying, counting, and tracking a specific animal species, plays a vital role in optimizing farm operations. However, dense occlusion among individuals in group activity scenarios remains a major challenge. To address this, we propose YOLO-SDD, a dense detection network designed for single-class densely populated scenarios. First, we introduce a Wavelet-Enhanced Convolution (WEConv) to improve feature extraction under dense occlusion. Following this, we propose an occlusion perception attention mechanism (OPAM), which further enhances the model's ability to recognize occluded targets by simultaneously leveraging low-level detailed features and high-level semantic features, helping the model better handle occlusion scenarios. Lastly, a Lightweight Shared Head (LS Head) is incorporated and specifically optimized for single-class dense detection tasks, enhancing efficiency while maintaining high detection accuracy. Experimental results on the ChickenFlow dataset, which we developed specifically for broiler detection, show that the n, s, and m variants of YOLO-SDD achieve AP
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