A novel daily behavior recognition model for cage-reared ducks by improving SPPF and C3 of YOLOv5s

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-10-29 DOI:10.1016/j.compag.2024.109580

Gen Zhang , Chuntao Wang , Deqin Xiao

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引用次数: 0

Abstract

Intensive duck farming can improve production efficiency and reduce environmental pollution. In modern intensive farming, ensuring the well-being and health of ducks is a paramount concern. Generally, the health status of ducks is determined by monitoring their daily behaviors, such as eating. However, research on cage-reared duck daily behavior recognition is scarce. Therefore, this study proposes a cage-reared duck daily behavior recognition model based on the improved YOLOv5s, denoted DBR-YOLOv5s for notational convenience. Specifically, to tackle the interfered features caused by the duck cage, an improved shrinkage mechanism is introduced in block SPPF of YOLOv5s. To decrease the feature information loss, the convolution operation replaces the maxpool operation in SPPF. Moreover, to cope with the issue of occlusion, the last three C3 blocks in module Neck of YOLOv5s are optimized via the multi-scale convolution operation, promoting the capability of DBR-YOLOv5s to extract contextual information. Extensive experiments were conducted on the self-constructed duck daily behavior dataset. The test precision of the proposed DBR-YOLOv5s is 92.8% for drinking, 96.8% for lying, 93.8% for standing, 98.5% for eating, 89.9% for preening, and 96.7% for spreading. Compared with the state-of-the-art YOLOv8x and YOLOv9c models, the average precision of DBR-YOLOv5s is 1.3% and 2.4% higher, respectively. The results indicate that the proposed DBR-YOLOv5s is effective for cage-reared duck daily behavior recognition, providing a non-contact method for duck daily behavior recognition.

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通过改进 YOLOv5s 的 SPPF 和 C3，建立新型笼养鸭日常行为识别模型

集约化养鸭可以提高生产效率，减少环境污染。在现代集约化养殖中，确保鸭子的福利和健康是最重要的问题。一般来说，鸭子的健康状况是通过监测其日常行为（如进食）来确定的。然而，有关笼养鸭日常行为识别的研究却很少。因此，本研究提出了一种基于改进型 YOLOv5s 的笼养鸭日常行为识别模型，为方便记述，将其命名为 DBR-YOLOv5s。具体来说，为了解决鸭笼对特征的干扰，在 YOLOv5s 的块 SPPF 中引入了改进的收缩机制。为了减少特征信息损失，卷积操作取代了 SPPF 中的 maxpool 操作。此外，为了应对遮挡问题，YOLOv5s 模块 Neck 中的最后三个 C3 块通过多尺度卷积操作进行了优化，从而提高了 DBR-YOLOv5s 提取上下文信息的能力。在自建的鸭子日常行为数据集上进行了广泛的实验。实验结果表明，DBR-YOLOv5s 的测试精度分别为：喝水 92.8%、躺卧 96.8%、站立 93.8%、吃东西 98.5%、打招呼 89.9%、撒欢 96.7%。与最先进的 YOLOv8x 和 YOLOv9c 模型相比，DBR-YOLOv5s 的平均精度分别提高了 1.3% 和 2.4%。结果表明，所提出的 DBR-YOLOv5s 能有效识别笼养鸭子的日常行为，为鸭子日常行为识别提供了一种非接触式方法。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.