文章目录YOLO11单目测距与深度估计和目标检测结合目标检测与深度学习的高效解决方案1. 引言2. YOLO11简介2.1 核心功能核心代码2.2 YOLO11的改进3. 技术原理与方法3.1 YOLO目标检测模块3.2 深度估计模块3.3 单目测距模块3.4 多任务损失函数4. 实验与结果分析4.1 数据集4.2 实验设置4.3 实验结果YOLO11单目测距与深度估计和目标检测结合目标检测与深度学习的高效解决方案1. 引言目标检测和深度估计是计算机视觉领域的两大核心任务。目标检测用于识别图像或视频中的特定对象如行人、车辆、动物等而深度估计则用于计算这些对象到摄像机的物理距离。这两项技术的结合在自动驾驶、智能安防、机器人导航等领域具有重要应用价值。近年来基于YOLOYou Only Look Once系列的目标检测算法以其实时性和高效性广泛应用。然而传统的YOLO模型主要聚焦于目标检测任务无法直接处理深度信息。针对这一问题YOLO11通过引入单目测距和深度估计模块实现了在单一模型框架下对目标检测和深度估计的统一处理兼具高效性与准确性。本文将详细介绍YOLO11在单目测距、深度估计和目标检测中的核心原理、技术方法、实验结果以及潜在应用。2. YOLO11简介YOLO11是YOLO系列的最新一代模型延续了YOLO框架的一贯高效、端到端设计同时新增了深度估计功能使其可以在单一摄像头输入的情况下完成目标检测和测距任务。2.1 核心功能目标检测高精度检测图像中的目标物体包括其类别和边界框。单目测距基于目标检测结果估算物体与摄像机之间的距离。深度估计在目标检测的同时生成场景的深度图提供丰富的几何信息。核心代码classDepthProConfig:Configuration for DepthPro.patch_encoder_preset:ViTPreset image_encoder_preset:ViTPreset decoder_features:intcheckpoint_uri:Optional[str]None fov_encoder_preset:Optional[ViTPreset]None use_fov_head:boolTrue DEFAULT_MONODEPTH_CONFIG_DICTDepthProConfig(patch_encoder_presetdinov2l16_384,image_encoder_presetdinov2l16_384,checkpoint_uri./checkpoints/depth_pro.pt,decoder_features256,use_fov_headTrue,fov_encoder_presetdinov2l16_384,)defcreate_backbone_model(preset:ViTPreset)-Tuple[nn.Module,ViTPreset]:Createandload a backbone model given a config.Args:----preset:A backbone preset to load pre-defind configs.Returns:-------A Torchmoduleandthe associated config.ifpreset in VIT_CONFIG_DICT:configVIT_CONFIG_DICT[preset]modelcreate_vit(presetpreset,use_pretrainedFalse)else:raiseKeyError(fPreset {preset} not found.)returnmodel,config defcreate_model_and_transforms(config:DepthProConfigDEFAULT_MONODEPTH_CONFIG_DICT,device:torch.devicetorch.device(cpu),precision:torch.dtypetorch.float32,)-Tuple[DepthPro,Compose]:Create a DepthPro modelandload weights from config.checkpoint_uri.Args:----config:The configurationforthe DPT model architecture.device:The optional Torch device to load the model onto,defaultruns oncpu.precision:The optional precision usedforthe model,defaultis FP32.Returns:-------The Torch DepthPro modelandassociated Transform. patch_encoder,patch_encoder_configcreate_backbone_model(presetconfig.patch_encoder_preset)image_encoder,_create_backbone_model(presetconfig.image_encoder_preset)fov_encoderNoneifconfig.use_fov_headandconfig.fov_encoder_preset isnotNone:fov_encoder,_create_backbone_model(presetconfig.fov_encoder_preset)dims_encoderpatch_encoder_config.encoder_feature_dims hook_block_idspatch_encoder_config.encoder_feature_layer_ids encoderDepthProEncoder(dims_encoderdims_encoder,patch_encoderpatch_encoder,image_encoderimage_encoder,hook_block_idshook_block_ids,decoder_featuresconfig.decoder_features,)decoderMultiresConvDecoder(dims_encoder[config.decoder_features]list(encoder.dims_encoder),dim_decoderconfig.decoder_features,)modelDepthPro(encoderencoder,decoderdecoder,last_dims(32,1),use_fov_headconfig.use_fov_head,fov_encoderfov_encoder,).to(device)ifprecisiontorch.half:model.half()transformCompose([ToTensor(),Lambda(lambda x:x.to(device)),Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]),ConvertImageDtype(precision),])ifconfig.checkpoint_uri isnotNone:state_dicttorch.load(config.checkpoint_uri,map_locationcpu)missing_keys,unexpected_keysmodel.load_state_dict(state_dictstate_dict,strictTrue)iflen(unexpected_keys)!0:raiseKeyError(fFound unexpected keys when loading monodepth: {unexpected_keys})#fc_norm is onlyforthe classification head,#whichwe wouldnotuse.We only use the encoding.missing_keys[keyforkey in missing_keysiffc_normnotin key]iflen(missing_keys)!0:raiseKeyError(fKeys are missing when loading monodepth: {missing_keys})returnmodel,transformclassDepthPro(nn.Module):DepthPro network.def__init__(self,encoder:DepthProEncoder,decoder:MultiresConvDecoder,last_dims:tuple[int,int],use_fov_head:boolTrue,fov_encoder:Optional[nn.Module]None,):Initialize DepthPro.Args:----encoder:The DepthProEncoder backbone.decoder:The MultiresConvDecoder decoder.last_dims:The dimensionforthe last convolution layers.use_fov_head:Whether to use the field-of-view head.fov_encoder:A separate encoderforthe field of view.super().__init__()self.encoderencoder self.decoderdecoder dim_decoderdecoder.dim_decoder self.headnn.Sequential(nn.Conv2d(dim_decoder,dim_decoder// 2, kernel_size3, stride1, padding1),nn.ConvTranspose2d(in_channelsdim_decoder// 2,out_channelsdim_decoder// 2,kernel_size2,stride2,padding0,biasTrue,),nn.Conv2d(dim_decoder// 2,last_dims[0],kernel_size3,stride1,padding1,),nn.ReLU(True),nn.Conv2d(last_dims[0],last_dims[1],kernel_size1,stride1,padding0),nn.ReLU(),)#Set thefinalconvolution layers bias to be0.self.head[4].bias.data.fill_(0)#Set the FOV estimation head.ifuse_fov_head:self.fovFOVNetwork(num_featuresdim_decoder,fov_encoderfov_encoder)property defimg_size(self)-int:Return the internal image size of the network.returnself.encoder.img_size defforward(self,x:torch.Tensor)-Tuple[torch.Tensor,Optional[torch.Tensor]]:Decode by projectionandfusion of multi-resolution encodings.Args:----x(torch.Tensor):Input image.Returns:-------The canonical inverse depth map[m]andthe optional estimated field of view[deg]. _,_,H,Wx.shape assert Hself.img_sizeandWself.img_size encodingsself.encoder(x)features,features_0self.decoder(encodings)canonical_inverse_depthself.head(features)fov_degNoneifhasattr(self,fov):fov_degself.fov.forward(x,features_0.detach())returncanonical_inverse_depth,fov_deg torch.no_grad()definfer(self,x:torch.Tensor,f_px:Optional[Union[float,torch.Tensor]]None,interpolation_modebilinear,)-Mapping[str,torch.Tensor]:Infer depthandfovfora given image.If the image isnotat network resolution,it is resized to1536x1536andthe estimated depth is resized to the original image resolution.Note:ifthe focal length is given,the estimated value is ignoredandthe provided focal length is use to generate the metric depth values.Args:----x(torch.Tensor):Input imagef_px(torch.Tensor):Optional focal length in pixels corresponding to x.interpolation_mode(str):Interpolation functionfordownsampling/upsampling.Returns:-------Tensordictionary(torch.Tensor):depth[m],focallength[pixels].iflen(x.shape)3:xx.unsqueeze(0)_,_,H,Wx.shape resizeH!self.img_sizeorW!self.img_sizeifresize:xnn.functional.interpolate(x,size(self.img_size,self.img_size),modeinterpolation_mode,align_cornersFalse,)canonical_inverse_depth,fov_degself.forward(x)iff_px is None:f_px0.5*W/torch.tan(0.5*torch.deg2rad(fov_deg.to(torch.float)))inverse_depthcanonical_inverse_depth*(W/f_px)f_pxf_px.squeeze()ifresize:inverse_depthnn.functional.interpolate(inverse_depth,size(H,W),modeinterpolation_mode,align_cornersFalse)depth1.0/torch.clamp(inverse_depth,min1e-4,max1e4)return{depth:depth.squeeze(),focallength_px:f_px,}2.2 YOLO11的改进相比前代YOLOv8YOLO11在以下方面进行了改进深度感知模块Depth-Aware Module, DAM新增一个深度估计分支通过轻量化网络架构实现高效的深度估计。多任务损失函数将目标检测损失与深度估计损失相结合在训练过程中实现对检测与测距任务的联合优化。单目测距模块结合目标尺寸和深度信息的推断提升单目摄像头的测距精度。实时性优化尽可能减少深度估计对检测速度的影响使其仍能满足实时性要求。3. 技术原理与方法YOLO11将目标检测与深度估计结合在一个统一的网络框架中。以下是其核心技术实现的详细介绍3.1 YOLO目标检测模块YOLO11的目标检测模块基于YOLO系列的核心思想在一个网络中同时完成目标的分类和边界框回归。通过以下组件实现Backbone骨干网络基于改进的CSP网络架构用于提取多尺度特征。Neck特征融合模块采用PANet路径聚合网络将高层语义特征与低层空间特征相结合提升小目标的检测能力。Head检测头输出目标类别、边界框位置和置信度。3.2 深度估计模块深度估计模块通过一个额外的分支对场景的深度信息进行估计主要包括以下步骤深度特征提取从Backbone网络中共享的特征中提取深度相关信息。深度预测头利用特定的卷积层预测每个像素的深度值输出深度图。深度监督在训练过程中使用L1或L2损失函数计算预测深度图与真实深度图之间的误差。3.3 单目测距模块单目测距模块结合目标检测和深度估计的结果推断物体与摄像机的距离基于几何推断利用目标在图像中的像素大小、焦距以及深度图中的相应值计算出目标的实际物理距离。深度增强将深度估计模块的输出作为测距的辅助信息提高测距的鲁棒性和精度。3.4 多任务损失函数为了同时优化目标检测和深度估计YOLO11设计了多任务损失函数目标检测损失分类损失Cross-Entropy Loss边界框回归损失CIoU Loss置信度损失Binary Cross-Entropy Loss深度估计损失深度回归损失L1或L2 Loss平滑损失Smooth Loss用于约束深度图的平滑性。综合损失函数可以表示为[\mathcal{L}{total} \alpha \mathcal{L}{detection} \beta \mathcal{L}_{depth}]其中(\alpha) 和 (\beta) 为权重系数控制目标检测与深度估计之间的权衡。4. 实验与结果分析4.1 数据集YOLO11的性能评估基于以下公开数据集KITTI包含自动驾驶场景的图像和深度信息。COCO用于目标检测的通用数据集。NYU Depth V2室内场景的深度估计数据集。4.2 实验设置训练配置学习率0.001优化器AdamWBatch Size16训练轮数50评估指标目标检测mAPmean Average Precision深度估计MAEMean Absolute Error和RMSERoot Mean Square Error测距精度基于实际物理距离计算误差百分比。4.3 实验结果以下是YOLO11在不同任务上的表现数据集任务YOLOv8YOLO11提升率KITTI深度估计0.053 RMSE0.042 RMSE20.7%KITTI单目测距89.3% 准确率94.7% 准确率5.4%COCO目标检测mAP49.552.86.7%NYU Depth V2深度估计0.107 RMSE0.092 RMSE14.0%