LSR-YOLO:一个高精度,轻量级的模型,用于在移动端的羊面部识别
Xiwen Zhang1,2, Chuanzhong Xuan1,2, Jing Xue1
1College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Animals : an open access journal from MDPI
|October 27, 2023
概括
一个新的轻量级羊面部识别模型,LSR-YOLO,为现代农业提供了准确和快速的识别. 这种模型显著降低了计算成本和尺寸,非常适合在绵羊养殖和福利领域的移动应用.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的羊鉴定对于畜牧业管理至关重要,包括养殖,疾病控制和福利.
- 传统的识别方法往往是低效的,昂贵的,不可靠的.
- 现有的羊面部识别模型面临着高计算需求,大尺寸和有限的实际应用.
研究的目的:
- 开发一种轻量级且实用的羊面部识别模型.
- 在计算成本,模型大小和性能方面解决现有模型的局限性.
- 提高农业环境中羊鉴定的效率和准确性.
主要方法:
- 提出了一个名为LSR-YOLO的新型轻量化绵羊面部识别模型.
- 使用了ShuffleNetv2和Ghost模块来优化YOLOv5s的功能提取骨干和部,减少FLOP和参数.
- 集成了一个协调注意力 (CA) 模块,以提高特征提取能力.
- 创建了一个羊脸数据集,使用63只小尾汉族羊的图像进行评估.
主要成果:
- 与YOLOv5s.相比,LSR-YOLO显示FLOP减少了25.5%,参数减少了33.4%.
- 在羊脸数据集中获得了97.8%的平均平均精度 (mAP@0.5).
- 模型尺寸被缩小到高效的9.5 MB.
- 成功地将LSR-YOLO集成到移动设备中,用于实时识别.
结论:
- 在羊识别准确性和模型大小方面,LSR-YOLO具有显著的优势.
- 该模型的轻量级设计和高性能使其适用于移动识别系统的实际应用.
- 对于移动识别,福利育种和整体牲畜管理,LSR-YOLO具有很高的应用价值.
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