Work

YOLO-SDD: An Effective Single-Class Detection Method for Dense Livestock Production

Year: 2025

Type: article

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 AP50:95 improvements of 2.18%, 2.13%, and 1.62% over YOLOv8n, YOLOv8s, and YOLOv8m, respectively. In addition, our model surpasses the detection performance of the latest real-time detector, YOLOv11. YOLO-SDD also achieves state-of-the-art performance on the publicly available GooseDetect and SheepCounter datasets, confirming its superior detection capability in crowded livestock settings. YOLO-SDD’s high efficiency enables automated livestock tracking and counting in dense conditions, providing a robust solution for precision livestock farming.

Source: Animals

DOI: 10.3390/ani15091205

Authors: Yubin Guo, Zhipeng Wu, Baihao You, Lanqi Chen, Jiangsan Zhao, Ximing Li

Institutions: South China Agricultural University, Norwegian Institute of Bioeconomy Research, Ministry of Agriculture and Rural Affairs

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,896