智能化农业装备学报（中英文）

基于GMA-YOLO11n的黄羽肉鸡行为多目标检测方法研究

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陈虹菲 ¹ , 王孙缘 ¹ , 于佳琪 ¹ , 刘俊岭 ² , 肖茂华 ³ , 钱燕 ¹ , 邹修国 ¹^,^*^,^*

智能化农业装备学报（中英文） | 2026,7(1): 43-51

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智能化农业装备学报（中英文） | 2026 , 7 (1) : 43 -51

基于GMA-YOLO11n的黄羽肉鸡行为多目标检测方法研究

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陈虹菲¹, 王孙缘¹, 于佳琪¹, 刘俊岭², 肖茂华³, 钱燕¹, 邹修国¹^,^*^,^*

作者信息

^1.南京农业大学智慧农业学院（人工智能学院），江苏南京，211800

^2.江苏深农智能科技有限公司，江苏南京，211800

^3.南京农业大学工学院，江苏南京，211800

通讯作者:

^* 邹修国，男，1979年生，博士，副教授，研究生导师；研究方向为动物表型及智能养殖（装备）技术。E-mail：zouxiuguo@njau.edu.cn

作者简介:

陈虹菲，2001年生，硕士，研究方向为动物表型及智能养殖（装备）技术。E-mail：2023119023@stu.njau.edu.cn

Multi-objective monitoring method of yellow-feathered broiler behavior based on GMA-YOLO11n model

Hongfei CHEN¹, Sunyuan WANG¹, Jiaqi YU¹, Junling LIU², Maohua XIAO³, Yan QIAN¹, Xiuguo ZOU¹

Affiliations

^1.College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 211800, China

^2.Jiangsu Shennong Intelligent Technology Co., Ltd., Nanjing 211800, China

^3.College of Engineering, Nanjing Agricultural University, Nanjing 211800, China

Corresponding Author:

^* ZOU Xiuguo, E-mail: zouxiuguo@njau.edu.cn

CHEN Hongfei, E-mail: 2023119023@stu.njau.edu.cn

出版时间: 2026-02-15 doi: 10.12398/j.issn.2096-7217.2026.01.005

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摘要

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通过日常巡检对肉鸡异常状态进行及时识别，是提升集约化养殖管理效率的重要手段。相比传统人工巡检方式，基于计算机视觉的自动化巡检在检测效率和一致性方面具有明显优势，但在实际养殖环境中，肉鸡个体密集分布，小目标、多尺度变化及遮挡现象普遍存在，给视觉检测模型的稳定应用带来挑战。针对上述问题，本研究基于YOLO11n（you only look once）模型，提出了一种改进的目标检测方法GMA-YOLO11n（GSConv and multi-scale attention YOLO11n）。该模型在Backbone中引入GSConv轻量化卷积模块以降低计算复杂度；并通过多尺度特征融合新增160×160的高分辨率特征层，以增强对小尺度和密集目标的检测能力；同时在多尺度特征输入前引入SE（squeeze-and-excitation）通道注意力模块，提升关键特征表达。试验结果表明，该模型能够有效实现肉鸡饮水、进食、行走等日常行为及异常状态的多类别检测，在数据集Ⅰ和数据集Ⅱ上的平均精度均值mAP分别达到93.87%和90.45%，较基线模型均有所提升，且推理速度满足实际视频巡检需求。

关键词

黄羽肉鸡 / 行为检测 / YOLO11n / 注意力机制 / 多尺度特征

Abstract

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Timely identification of abnormal conditions in broiler chickens through routine inspections is a crucial means to improve the efficiency of intensive poultry farming management. In intensive poultry farming, computer vision-based inspection technologies outperform traditional manual methods in both accuracy and efficiency, showing significant potential for development. However, in large-scale farms, challenges such as multi-scale targets and occlusion pose considerable difficulties for models. To address these issues, this study proposes an improved object detection method, termed GMA-YOLO11n (GSConv and multi-scale attention YOLO11n), based on the YOLO11n (you only look once) framework. Specifically, a GSConv lightweight convolution module is introduced into the Backbone to reduce computational complexity, and a high-resolution feature layer of 160×160 is added through multi-scale feature fusion to enhance the detection of small-scale and densely distributed targets. In addition, a squeeze-and-excitation (SE) channel attention module is incorporated before multi-scale feature inputs to strengthen the representation of key features. Experimental results demonstrate that the proposed model can effectively perform multi-class detection of daily behaviors, including drinking, feeding, and walking, as well as abnormal states of broiler chickens. The model achieves mean average precision values of 93.87% and 90.45% on Dataset I and Dataset Ⅱ , respectively, both outperforming the baseline model, while maintaining an inference speed that satisfies practical video inspection requirements.

Key words

yellow-feathered broilers / behavior detection / YOLO11n / attention mechanism / multi-scale features

引用本文

陈虹菲, 王孙缘, 于佳琪, 刘俊岭, 肖茂华, 钱燕, 邹修国. 基于GMA-YOLO11n的黄羽肉鸡行为多目标检测方法研究. 智能化农业装备学报（中英文）, 2026 , 7 (1) : 43 -51 . DOI: 10.12398/j.issn.2096-7217.2026.01.005

Hongfei CHEN, Sunyuan WANG, Jiaqi YU, Junling LIU, Maohua XIAO, Yan QIAN, Xiuguo ZOU. Multi-objective monitoring method of yellow-feathered broiler behavior based on GMA-YOLO11n model[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (1) : 43 -51 . DOI: 10.12398/j.issn.2096-7217.2026.01.005

基金

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江苏省国际合作项目(BZ2023013)
国家重点研发子课题(2024YFD200030204)

参考文献

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2026年第7卷第1期

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文章信息

doi: 10.12398/j.issn.2096-7217.2026.01.005

接收时间：2025-12-02
首发时间：2026-11-03
出版时间：2026-02-15

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收稿日期：2025-12-02
修回日期：2026-01-12

基金

Jiangsu Provincial International Cooperation Project(BZ2023013)

江苏省国际合作项目(BZ2023013)

Sub-project of the National Key Research and Development Program of China(2024YFD200030204)

国家重点研发子课题(2024YFD200030204)

作者信息

^1.南京农业大学智慧农业学院（人工智能学院），江苏南京，211800

^2.江苏深农智能科技有限公司，江苏南京，211800

^3.南京农业大学工学院，江苏南京，211800

通讯作者:

^* 邹修国，男，1979年生，博士，副教授，研究生导师；研究方向为动物表型及智能养殖（装备）技术。E-mail：zouxiuguo@njau.edu.cn

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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