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生态检测-YOLOv2:在复杂的监控环境中进行多尺度废物检测的高性能模型
Jing Su1, Ruihan Chen1,2, Mingzhi Li1
1School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了EcoDetect-YOLOv2,这是一种先进的废物检测模型,可以在复杂的环境中显著提高准确性. 它为自动化废物监测和城市治理提供了更强大,更有效的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 传统的废物监测依赖于手动方法,这些方法效率低下,容易出现错误.
- 现有的物体检测模型因混乱的背景,尺度变化和小物体大小而难以与现实世界监控数据相匹配.
- 需要强大,高效和可扩展的解决方案,用于复杂环境中的自动废物检测.
研究的目的:
- 开发一种轻量级,高效的物体检测模型,用于现实世界废物监测.
- 加强在复杂的环境中检测小型和多类废物.
- 提高废物检测模型的稳定性和通用性,以应对环境噪音和尺度变化.
主要方法:
- 推出了基于YOLOv8s架构的EcoDetect-YOLOv2模型,其中包含了针对小物体的P2检测层.
- 集成了一种高效的多尺度注意力 (EMA) 机制和一个动态采样模块 (Dysample) 以改进特征表示.
- 用Ghost Convolution (GhostConv) 取代了传统的卷积层,并提出了GhostResBottleneck和ResGhostCSP模块,以减少计算开销.
- 利用复杂环境废物暴露检测 (IEWED) 数据集,以复杂的现实场景进行培训和评估.
主要成果:
- 在IEWED数据集上,EcoDetect-YOLOv2表现出优于基线YOLOv8s的性能.
- 在精度方面取得了1.0%的改进,在回忆方面提高了4.6%,在mAP50方面提高了4.8%,在mAP50方面提高了3.1%:95.
- 将参数数量减少了19.3%,同时保持或提高了检测准确度.
- 展示了对小物体的增强敏感性和对杂乱的背景和尺度变化的坚固性.
结论:
- EcoDetect-YOLOv2是一个有效和高效的模型,用于复杂环境中的实时,多物体废物检测.
- 该模型为自动化城市废物管理和数字治理提供了一个可扩展的解决方案.
- 拟议的架构修改增强了检测能力,降低了计算负载,使其适合实际部署.
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