EVIT-YOLOv8: Construction and research on African Swine Fever facial expression recognition

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-10-25 DOI:10.1016/j.compag.2024.109575

Lili Nie , Bugao Li , Fan Jiao , Wenjuan Lu , Xinlong Shi , Xinyue Song , Zeya Shi , Tingting Yang , Yihan Du , Zhenyu Liu

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引用次数: 0

Abstract

The threats of infectious diseases such as African Swine Fever, Swine Erysipelas, and Blue Ear Disease to the pig farming industry have been increasing year by year. Producers often face difficulties in diagnosis, leading to the misuse of measures and the spread of epidemics. Addressing this, the proposed EVIT-YOLOv8 model integrates the EViT module to surpass ViT limitations and incorporates the CBAM module for enhanced image feature representation. Employing the GIOU loss function ensures better precision in capturing facial expression features, yielding an impressive Mean Average Precision (mAP) of 86.6% in differentiation tasks. Specifically, in African Swine Fever facial expression recognition, the model achieves a remarkable Precision of 85.2%, outperforming YOLOv5, YOLOv7, and YOLOv8 models by 6%, 23.5%, and 7.3%, respectively. This provides pig producers with a precise diagnostic tool, mitigating the risk of epidemic spread due to misdiagnosis and facilitating effective prevention and control of infectious diseases.

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EVIT-YOLOv8：非洲猪瘟面部表情识别的构建与研究

非洲猪瘟、猪红痢和蓝耳病等传染病对养猪业的威胁逐年增加。生产者往往在诊断方面遇到困难，导致措施的滥用和流行病的蔓延。针对这一问题，提出的 EVIT-YOLOv8 模型集成了 EViT 模块，以超越 ViT 的限制，并结合了 CBAM 模块以增强图像特征表示。采用 GIOU 损失函数可确保更精确地捕捉面部表情特征，在区分任务中取得了令人印象深刻的 86.6% 平均精确度 (mAP)。具体来说，在非洲猪瘟面部表情识别中，该模型的精确度达到了 85.2%，分别比 YOLOv5、YOLOv7 和 YOLOv8 模型高出 6%、23.5% 和 7.3%。这为养猪生产者提供了精确的诊断工具，降低了因误诊而导致流行病传播的风险，促进了传染病的有效预防和控制。

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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.