Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundan...Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.展开更多
图像目标检测常受复杂背景干扰,导致目标特征模糊、误检率高及检测速度慢等问题。为此,开展了基于机器视觉的复杂背景干扰图像目标检测方法研究。首先,设计了基于机器视觉的复杂背景干扰图像照明优化装置,通过照明设计有效减少复杂背景...图像目标检测常受复杂背景干扰,导致目标特征模糊、误检率高及检测速度慢等问题。为此,开展了基于机器视觉的复杂背景干扰图像目标检测方法研究。首先,设计了基于机器视觉的复杂背景干扰图像照明优化装置,通过照明设计有效减少复杂背景对目标识别的干扰,提高目标对象的可见度;其次,改进了PPYOLOv2(pre-processing you only look once version 2)网络模型,通过优化预处理、特征提取、特征融合、预测Head、预测结果生成、损失计算与优化以及检测与后处理等7个关键步骤,实现了对复杂背景干扰图像中目标的准确、高效检测。测试结果表明:设计方法未出现漏检与误检现象,检测效果表现良好,每个设计步骤均能提升实验目标检测精度,说明每个设计步骤对于复杂背景都是必要的。展开更多
文摘Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.
文摘图像目标检测常受复杂背景干扰,导致目标特征模糊、误检率高及检测速度慢等问题。为此,开展了基于机器视觉的复杂背景干扰图像目标检测方法研究。首先,设计了基于机器视觉的复杂背景干扰图像照明优化装置,通过照明设计有效减少复杂背景对目标识别的干扰,提高目标对象的可见度;其次,改进了PPYOLOv2(pre-processing you only look once version 2)网络模型,通过优化预处理、特征提取、特征融合、预测Head、预测结果生成、损失计算与优化以及检测与后处理等7个关键步骤,实现了对复杂背景干扰图像中目标的准确、高效检测。测试结果表明:设计方法未出现漏检与误检现象,检测效果表现良好,每个设计步骤均能提升实验目标检测精度,说明每个设计步骤对于复杂背景都是必要的。
