ArticleAnimals : an open access journal from MDPI2025
Occlusion-Aware Caged Chicken Detection Based on Multi-Scale Edge Information Extractor and Context Fusion.
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 1 paper.
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Who cites it
1 citing paper in PubMed.
- GMS-YOLO11n: A Sheep Detection Model for Challenging Fixed-View Farm Conditions Integrating Spatially Gated Structural Enhancement and Multi-Scale Attention.Animals : an open access journal from MDPI · 2026Article
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Authors and funding
7 authors.
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Abstract
Due to the complex environment of caged chicken coops, uneven illumination and severe occlusion in the coops lead to unsatisfactory accuracy of chicken detection. In this study, we construct an image dataset in the production environment of caged chickens using a head and neck co-annotation method and a multi-stage co-enhancement strategy, and we propose Chicken-YOLO, an occlusion-aware caged chicken detection model based on multi-scale edge information extractor and context fusion for the severe occlusion and poor illumination situations. The model enhances chicken feather texture and crown contour features via the multi-scale edge information extractor (MSEIExtractor), optimizes downsampling information retention through integrated context-guided downsampling (CGDown), and improves occlusion perception using the detection head with the multi-scale separation and enhancement attention module (DHMSEAM). Experiments demonstrate that Chicken-YOLO achieves the best detection performance among mainstream models, exhibiting 1.7% and 1.6% improvements in
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