绿色果实探测器:在果园环境中使用的轻量级绿色果实探测器
Jing Wang1, Yu Shang1, Xiuling Zheng1
1School of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang, China.
PloS one
|November 14, 2024
概括
我们开发了GreenFruitDetector,这是一种轻量级的人工智能模型,用于检测绿色水果,在具有挑战性的数据集上,精度提高了1.77%. 该模型增强了特征提取和多尺度检测,以更好地监控果园.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 绿色水果的检测是具有挑战性的,因为它被叶子伪装.
- 现有的模型经常在果园中与特征提取和多尺度对象检测作斗争.
研究的目的:
- 开发一种轻量级和高效的模型,用于准确检测绿色水果.
- 为了改善特征提取,特别是在阻塞下,并增强检测小/遥远的果实.
主要方法:
- 一个改进的YOLO v8架构,包括可变形卷积和MCAG-DC (多路径坐标注意力引导变形卷积).
- 一个融合子结构,用于整合多个尺度的空间信息.
- 一个新的检测头,设计用于多尺度物体检测.
- 道修剪技术,以减少模型大小和计算成本.
主要成果:
- 获得的准确率为94.5% (科拉梨),84.4% (瓜) 和85.9% (绿果).
- 与基线模型相比,准确度得到了1.17%,1.1%和1.77%的改进.
- 增加了mAP@0.5高达6.5%,并召回高达1.97%.
- 模型尺寸,参数和FLOP分别减少了50%,55%和44%.
结论:
- 绿色果实检测器模型显著提高了绿色果实检测的准确性和效率.
- 建筑改进有效地解决了诸如颜色相似性和遮等挑战.
- 优化的模型为精密农业应用提供了实用解决方案.
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