基于改进的YOLOX-S模型的复杂环境中废物成分的多目标检测
Rui Zhao1, Qihao Zeng1, Liping Zhan1
1School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu 611756, China.
Waste management (New York, N.Y.)
|October 15, 2024
概括
本研究介绍了一种增强的"只看一次" (YOLOX-S) 模型,用于在复杂环境中准确识别废物,通过更好的目标检测改善可持续固体废物管理.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 准确的废物识别对于可持续的固体废物管理至关重要.
- 在区分废物类别方面存在挑战,原因是多目标检测中的功能不完整.
研究的目的:
- 提出一个改进的You Only Look Once (YOLOX-S) 模型,用于在复杂场景中有效识别废物组件.
- 增强特征提取,提高固体废物管理的检测准确度.
主要方法:
- 开发了一种改进的YOLOX-S模型,包括一个卷积块注意力模块,自适应空间特征融合,以及一个高效的交叉对联损失函数.
- 在一个由自己构建的数据集上训练模型,其中包括各种废物组件和复杂的环境干扰.
- 在定制和公共数据集上对原始YOLO和经典模型 (SVM,ResNet-18,ResNet-50) 进行性能评估.
主要成果:
- 改进的YOLOX-S模型在定制数据集上实现了85.02%的平均平均精度 (mAP),比原始YOLO增加了5.32%.
- 证明了不准确定位,错误检测和错误检测的减少情况.
- 在公开数据集上表现优于经典模型,mAP为94.85%.
结论:
- 增强的YOLOX-S模型在具有挑战性的条件下显著提高了废物组件识别的准确性.
- 该模型提供了一个强大的解决方案,用于智能监控废物,帮助管理不分青红白的处置和非法倾倒.
- 在固体废物场景中为紧急管理提供了有价值的决策支持.
相关概念视频
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A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
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