[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fPXZ3ztRAUmZjZqv_AAP7hTKHccFBgXDAAw9Dy82INNE":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2025-05-06 18:24:29",185940931,[8,9,10,11],"目标置信度损失","边界框回归损失","分类损失","以上全部",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},27,"034a2b5846d1eb1c9da37ec2b6f32f52","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5db85478aa0dbac2d08489d9652f8926.png","智能图像处理","2370395865aa4ed0bf00c5e04710cb3c","","YOLOv5的损失函数包括______",[21,32,41,50,59,68,77,80,89,98],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],185940925,[25,26,27,28],"单阶段式","双阶段式","基于传统方法","基于手工特征","Faster R-CNN 是一种______目标检测方法","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],185940926,[36,37,38,39],"生成检测框","计算损失函数","对图像进行分类","生成图像的特征图","在目标检测中,候选区域网络(RPN)的作用是",{"answer":42,"createTime":5,"id":43,"options":44,"question":49,"source":30,"type":31},[],185940927,[45,46,47,48],"将图像进行缩放","将候选区域的大小归一化","生成候选区域框","进行分类预测","在Faster R-CNN中,兴趣域池化(ROI pooling)的主要作用是",{"answer":51,"createTime":5,"id":52,"options":53,"question":58,"source":30,"type":31},[],185940928,[54,55,56,57],"确定目标类别","调整候选框的位置和尺寸","计算目标的置信度","增强图像特征","Faster R-CNN中的边界框回归(Bounding Box Regression)用于______",{"answer":60,"createTime":5,"id":61,"options":62,"question":67,"source":30,"type":31},[],185940929,[63,64,65,66],"提高检测框的数量","删除冗余的预测框","增加检测精度","减少网络的复杂性","目标检测中,非极大值抑制(NMS)用于______",{"answer":69,"createTime":5,"id":70,"options":71,"question":76,"source":30,"type":31},[],185940930,[72,73,74,75],"模型的训练时间","模型在所有类别上的平均检测性能","模型的分类精度","目标的定位精度","在目标检测中,"mean average precision"(mAP)是用来衡量______",{"answer":78,"createTime":5,"id":6,"options":79,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":81,"createTime":5,"id":82,"options":83,"question":88,"source":30,"type":31},[],185940932,[84,85,86,87],"L2损失","CIoU损失","交叉熵损失","MSE损失","在YOLOv5中,检测框回归损失使用______作为损失函数",{"answer":90,"createTime":5,"id":91,"options":92,"question":97,"source":30,"type":31},[],185940933,[93,94,95,96],"RNN","卷积神经网络(CNN)","循环神经网络(LSTM)","生成对抗网络(GAN)","在Faster R-CNN中,特征提取部分使用了______",{"answer":99,"createTime":100,"id":101,"options":102,"question":103,"source":30,"type":31},[],"2025-05-06 18:30:16",185943567,[25,28,27,26],"Faster R-CNN 是一种______目标检测方法."]