CN115512173A - 用于使用增广数据训练机器模型的系统和方法 - Google Patents

用于使用增广数据训练机器模型的系统和方法 Download PDF

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CN115512173A
CN115512173A CN202211241885.5A CN202211241885A CN115512173A CN 115512173 A CN115512173 A CN 115512173A CN 202211241885 A CN202211241885 A CN 202211241885A CN 115512173 A CN115512173 A CN 115512173A
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M·J·库珀
P·贾因
H·S·赛杜
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Abstract

用于使用增广数据训练机器模型的系统和方法。一种示例方法包括标识由一组相机在固定到一个或多个图像收集系统的同时捕获的一组图像。对于该组图像中的每个图像，标识该图像的训练输出。对于该组图像中的一个或多个图像，生成一组经增广的图像的经增广的图像。生成经增广的图像包括使用保持图像的相机属性的图像操纵功能来修改图像。将增广的训练图像与图像的训练输出相关联。基于包括图像和该组经增广的图像的图像训练集来训练预测计算机模型的一组参数以预测训练输出。

Description

用于使用增广数据训练机器模型的系统和方法
本申请是国际申请号为PCT/US2019/055683、国际申请日为2019年10月10日、于2021年05月18日进入中国国家阶段、中国国家申请号为201980075941.2、发明名称为“用于使用增广数据训练机器模型的系统和方法”的发明专利申请的分案申请。
相关申请的交叉引用
本申请要求于2018年10月11日提交的题为“TRAINING MACHINE MODELS WITHDATA AUGMENTATION THAT RETAINS SENSOR CHARACTERISTICS”的美国临时申请第62/744,534号的优先权。美国临时申请第62/744,534号的全部内容通过引用并入本文。
背景技术
本发明的实施例总体上涉及用于在机器学习环境中训练数据的系统和方法，并且更具体地涉及通过在训练数据集中包括诸如传感器特性等附加数据来增广训练数据。
在典型的机器学习应用中，可以以各种方式来增广数据以避免使模型过度适合用于获取训练数据的捕获设备的特性。例如，在用于训练计算机模型的图像的典型集合中，图像可以表示利用很多不同捕获环境而捕获的对象，这些捕获环境相对于被捕获的对象具有变化的传感器特性。例如，这样的图像可以通过各种传感器特性，诸如各种比例(例如，图像内的明显不同的距离)，利用各种焦距，通过各种透镜类型，利用各种预处理或后处理的不同软件环境、传感器阵列硬件等来捕获。这些传感器还可能在不同外部参数方面有所不同，诸如在捕获图像时成像传感器相对于环境的位置和方向。所有这些不同类型的传感器特性都可能导致捕获图像在图像集中的整个不同图像中以不同且多样化方式呈现并且使得更难以正确地训练计算机模型。
神经网络的很多应用从在各种条件下捕获的数据中学习，并且被部署在各种不同传感器配置上(例如，在多种类型的移动电话上运行的应用中)。为了解决用于捕获图像的传感器之间的差异，开发人员可以使用诸如翻转、旋转或裁剪图像等修改来经增广的图像训练数据，从而针对相机属性(诸如焦距、轴偏斜、位置和旋转)来概括所开发的模型。
为了解决这些变化并且将已训练网络部署在各种源上，可以增广或操纵训练数据以增加已训练模型的鲁棒性。但是，这些方法通常会通过应用修改经增广的图像中相机属性的转换来阻止模型针对任何特定相机配置有效地学习。
发明内容
一个实施例是一种用于训练预测计算机模型的一组参数的方法。该实施例可以包括：标识由一组相机在固定到一个或多个图像收集系统的同时捕获的一组图像；对于该组图像中的每个图像，标识图像的训练输出；对于该组图像中的一个或多个图像，通过以下方式生成一组经增广的图像的经增广的图像：通过使用保持图像的相机属性的图像操纵功能修改图像来生成一组经增广的图像的经增广的图像，以及将增广的训练图像与图像的训练输出相关联；基于包括图像和该组经增广的图像的图像训练集来训练预测计算机模型的该组参数集以预测训练输出。
另一实施例可以包括一种系统，该系统具有一个或多个处理器和存储指令的非暂态计算机存储介质，该指令在由一个或多个处理器执行时引起处理器执行操作，该操作包括：标识由一组相机在固定到一个或多个图像收集系统的同时捕获的一组图像；对于该组图像中的每个图像，标识图像的训练输出；对于该组图像中的一个或多个图像，通过以下方式生成一组经增广的图像的经增广的图像：通过使用保持图像的相机属性的图像操纵功能修改图像来生成一组经增广的图像的经增广的图像，以及将经增广的训练图像与图像的训练输出相关联；基于包括图像和该组经增广的图像的图像训练集来训练预测计算机模型的该组参数集以预测训练输出。
另一实施例可以包括一种具有用于由处理器执行的指令的非暂态计算机可读介质，该指令在由处理器执行时引起处理器：标识由一组相机在固定到一个或多个图像收集系统的同时捕获的一组图像；对于该组图像中的每个图像，标识图像的训练输出；对于该组图像中的一个或多个图像，通过以下方式生成一组经增广的图像的经增广的图像：通过使用保持图像的相机属性的图像操纵功能修改图像来生成一组经增广的图像的经增广的图像，以及将经增广的训练图像与图像的训练输出相关联；基于包括图像和该组经增广的图像的图像训练集来训练计算机模型学习预测训练输出。
附图说明
图1是根据一个实施例的用于计算机模型训练和部署的环境的框图。
图2示出了使用相同相机特性捕获的示例图像。
图3是根据一个实施例的模型训练系统的组件的框图。
图4是示出根据一个实施例的基于所标记的训练图像来生成经增广的图像的示例的数据流程图。
附图仅出于说明的目的描绘了本发明的各种实施例。本领域技术人员将从以下讨论中容易认识到，在不脱离本文中描述的本发明的原理的情况下，可以采用本文所示的结构和方法的替代实施例。
具体实施方式
一个实施例是一种系统，该系统利用已经被增广以保持原始捕获图像的相机属性的图像来训练计算机模型。这些相机属性可以包括相机的固有或外部属性。这样的固有属性可以包括传感器本身的特性，诸如动态范围、视场、焦距和透镜畸变。外部属性可以描述相机相对于捕获环境的配置，诸如相机的角度、比例或姿势。
这些固有和外部属性可能会影响相机相对于在图像中捕获的对象和其他方面以及伪像和其他效果(诸如由于其在设备或系统上的位置而出现在相机视图中的静态对象)的视图。例如，安装在车辆上的相机可以包括汽车的引擎盖作为其视图的一部分，该引擎盖会出现在很多图像上，并且对于以相同方式安装在同一型号汽车上的这种配置的所有相机都出现。作为另一示例，这些相机属性还可以包括从相机视图内的对象发出的反射。反射可以是一种类型的一致的特性，该特性被包括在由相机捕获的很多图像中。
通过保持、保存、存储或使用图像的相机属性来训练数据模型，同时仍向训练数据添加经增广的图像，所得到的模型在具有相同相机属性的很多不同设备中可能是有用的。而且，增广可以为模型预测提供一般性和更大的鲁棒性，尤其是当图像被模糊、遮挡或以其他方式不能提供可检测对象的清晰视图时。这些方法对于对象检测和自动驾驶车辆可能特别有用。这种方法对于其他情况也可能是有益的，在这些情况下，相同相机配置可以部署到很多设备。由于这些设备可能在一致取向上具有一组一致传感器，因此可以使用给定配置来收集训练数据，可以使用来自所收集的训练数据的增广数据来训练模型，并且可以将已训练模型部署到具有相同配置的设备。因此，这些技术避免了在这种情况下产生不必要的概括的增广，并且允许通过某些数据增广来对其他变量进行概括。
为了保持相机属性，用于生成经增广的图像的图像操纵功能是一种用于保持相机属性的功能。例如，这些操纵可以避免影响相机相对于捕获环境的角度、比例或姿势。在实施例中，没有在训练中使用通过影响相机属性的图像操纵功能来增广的图像。例如，可以用于保持相机属性的图像操纵功能包括切除、色度/饱和度/值抖动、椒盐噪声以及域转移(例如，昼夜修改)。可以修改相机属性并且因此在某些实施例上不使用的那些功能包括裁剪、填充、翻转(水平或垂直)或仿射变换(诸如剪切、旋转、平移和倾斜)。
作为另一示例，可以利用去除原始图像的一部分的“切除”功能来经增广的图像。然后，可以将图像的已去除部分替换为其他图像内容(诸如指定的颜色、模糊、噪声或另一图像)。切除的数目、大小、区域和替换内容可以变化，并且可以基于图像的标签(例如，图像中的感兴趣区域或对象的边界框)。
因此，可以利用图像和经增广的图像来训练计算机模型，并且将计算机模型分配给具有捕获图像的相机特性的设备以在传感器分析中使用该模型。特别地，该数据增广和模型训练可以用于被训练以检测图像中的对象或对象边界框的模型。
图1是根据一个实施例的用于计算机模型训练和部署的环境。一个或多个图像收集系统140捕获可以由模型训练系统在训练计算机模型时使用的图像，该计算机模型可以由模型应用系统部署和使用。这些系统经由网络120(诸如互联网)连接，网络120代表这些设备通过其进行通信的各种无线或有线通信链路。
模型训练系统130训练具有一组可训练参数的计算机模型，以在给定一组输入的情况下预测输出。在该示例中，模型训练系统130通常基于图像输入来训练模型，以生成预测关于图像的信息的输出。例如，在各种实施例中，这些输出可以标识图像中的对象(通过边界框或分段来标识对象，可以标识图像的条件(例如，一天中的时间、天气)或图像的其他标签或描述符。
尽管为了方便起见在本文中将图像用作传感器数据的示例类型，但是如本文中描述的增广和模型开发可以应用于多种类型的传感器，以增广从这些传感器捕获的训练数据，同时保持传感器配置特性。
图像收集系统140具有一组传感器，该组传感器从图像收集系统140的环境中捕获信息。尽管示出了一个图像收集系统140，但是很多图像收集系统140可以捕获用于模型训练系统130的图像。用于图像收集系统140的传感器具有在图像收集系统140上可以相同或基本相同的传感器特性。在一个实施例中，图像收集系统是在环境中移动并且用相机捕获环境的图像的车辆或其他系统。图像收集系统140可以是手动操作的，或者可以是部分或全自动操作的。因此，当图像收集系统140遍历环境时，图像收集系统140可以捕获环境的图像并且将其传输到模型训练系统130。
模型应用系统110是具有一组传感器的系统，该组传感器具有与图像收集系统相同或基本相同的传感器特性。在一些示例中，模型应用系统110还用作图像收集系统130，并且将所捕获的传感器数据(例如，图像)提供给模型训练系统130以用作另外的训练数据。模型应用系统110从模型训练系统130接收已训练模型，并且将模型与其传感器感测到的数据一起使用。因为从图像收集系统140和模型应用系统110捕获的图像具有相同的相机配置，所以模型应用系统110可以以与图像收集系统相同的方式和从相同的角度(或基本相似)来捕获其环境。在应用模型之后，模型应用系统110可以将模型的输出用于各种目的。例如，当模型应用系统110是车辆时，模型可以预测图像中对象的存在，模型应用系统110可以将其用作安全系统的一部分或自治系统(或半自治)控制系统的一部分。
图2示出了使用相同相机特性捕获的示例图像。在该示例中，图像200A由图像收集系统130上的相机捕获。另一图像200B也可以由图像收集系统130捕获，该图像收集系统130可以相同或可以是不同的图像收集系统130。当捕获不同环境和环境内的不同对象时，这些图像相对于捕获环境的图像保持相机属性。相机属性是指影响环境在相机中的外观的相机的配置和取向属性。例如，这些相机属性可以包括相机相对于环境的角度、比例和姿势(例如，观看位置)。相对于捕获图像的相同环境来修改相机的角度、比例或位置会导致环境的图像发生变化。例如，放置在较高位置的相机将从不同高度查看对象，并且将示出该对象的与较低位置不同的部分。同样，由于不属于要分析的环境的相机配置，这些图像在图像中包括一致的伪像和效果。例如，图像200A和200B都包括来自挡风玻璃的眩光和其他效果，图像右下侧的对象遮挡环境，并且挡风玻璃遮挡图像的底部。因此，从相同相机特性捕获的图像通常呈现相同的伪像、失真，并且以相同方式捕获环境。
图3示出了根据一个实施例的模型训练系统130的组件。模型训练系统包括用于训练计算机模型的各种模块和数据存储部。模型训练系统130通过增广来自图像收集系统140的图像以改善模型的概括来训练供模型应用系统110使用的模型。经增广的图像用不影响(例如，保持)图像的相机配置的图像操纵功能来生成。这允许进行更有效的建模，同时允许对模型参数进行概括，从而更选择性地避免针对在图像之间可能存在差异的图像的各方面进行过度拟合，同时允许模型参数更紧密地学习与一致的相机特性相关的权重。
模型训练系统包括数据输入模块310，该数据输入模块310从图像收集系统140接收图像。数据输入模块310可以将这些图像存储在图像数据存储部350中。数据输入模块310可以接收由数据收集系统140生成或提供的图像，或者可以从图像收集系统140请求图像。
标记模块320可以标识标签或将标签应用于图像数据350中的图像。在一些示例中，图像可能已经具有所标识的特性。标签还可以表示将由已训练模型预测或输出的数据。例如，标签可以指定图像中所示的环境中的特定对象，或者可以包括与图像相关联的描述符或“标签”。取决于模型的应用，标签可以以各种方式表示该信息。例如，对象可以与图像内的边界框相关联，或者可以从图像的其他部分中分割出对象。因此，所标记的图像可以表示训练模型所依据的地面真实情况。可以通过任何合适的方式来标记图像，并且通常可以通过有监督标记过程来标记图像(例如，由用户通过查看图像并且为图像指定标签来标记)。这些标签然后可以与图像数据存储部350中的图像相关联。
图像增广模块330可以基于由图像收集系统140捕获的图像来生成附加图像。这些图像可以作为模型训练模块340的训练管线的一部分被生成，或者这些经增广的图像可以在模型训练模块340中开始训练之前生成。经增广的图像可以基于由图像收集系统140捕获的图像来生成。
图4示出了根据一个实施例的基于所标记的训练图像400的经增广的图像的示例生成。所标记的训练图像可以是由图像收集系统140捕获的图像。训练图像410可以包括未增广的训练图像410A，该训练图像410A具有与所标记的训练图像400中的标记数据相对应的相关联的训练输出420A。
图像增广模块330通过将图像操纵功能应用于所标记的训练图像400来生成经增广的图像。图像操纵功能生成所标记的训练图像400的修改版本以改变用于训练模型的图像的特性。用于生成训练图像的图像操纵功能保持所标记的训练图像400的相机属性。因此，在捕获可能在各种设备上保持一致的环境时，操纵功能可以保持可能受相机的物理捕获特性或相机的位置影响的环境的视图的比例、透视图、取向和其他特性。因此，图像操纵功能可以影响环境的对象或其他特征在场景中的可见程度或者在场景中被看到的清晰程度，但不影响图像中对象的位置或大小。保持相机特性的可以应用的示例图像操纵功能包括切除、抖动(例如，用于色度、饱和度或色彩值)、椒盐噪声(引入黑白点)、模糊和域转移。这些图像操纵功能中的一个以上的图像操纵功能可以组合应用以生成经增广的图像。切除是指一种图像操纵功能，它可以去除图像的一部分并且将去除部分替换为其他图像内容。域转移是指将图像修改为与图像中的另一环境条件相对应的图像操纵功能。例如，可以修改白天的图像以近似图像在晚上看起来如何，或者可以修改在阳光下捕获的图像以增加下雨或下雪的效果。
这些经增广的图像可以与与所标记的训练图像400相同的训练输出相关联。在图4所示的示例中，经增广的图像410B是通过对所标记的训练图像400施加切除而生成的，并且经增广的图像410B可以与训练输出420B相关联。类似地，为了生成训练图像410C，应用多个切除以修改图像的各部分。在该示例中，应用以生成训练图像410C的切除用不同图案填充图像的切除区域。
在各种实施例中，可以向切除施加各种参数和配置，其可以基于训练图像和训练输出在图像中的位置而变化。因此，切除的数目、大小、位置和替换图像内容可以在不同实施例中基于训练输出的位置而变化。作为示例，切除功能可以应用相似大小的多个切除，或者可以应用范围内不同的半随机化大小的多个切除。通过使用多个切除并且改变大小，切除可以更紧密地模拟真实世界障碍物(具有各种大小)在查看对象时的效果，并且可以阻止已训练模型学习补偿任何一种特定大小的切除。
切除的尺寸范围可以基于图像内的对象或其他标签的尺寸的一部分。例如，切除可能不超过图像中对象边界框大小的40％，或者小于最小对象边界框的大小。这可以确保切除不会完全遮挡目标对象，并且因此确保图像将继续包括模型可以从中学习的对象的图像数据。切除的数目也可以被随机化，并且可以从诸如均匀、高斯或指数分布等分布中选择。
另外，可以基于图像中对象的位置来选择切除的位置。这可以提供与边界框的一些但不是过多的重叠。对象与切除区域之间的交集可以通过对象的被切除替换的部分来测量，或者可以通过结合交集(IoU)来测量，该IoU可以通过对象与切除区域的交集除以对象区域与切除区域的并集来测量。例如，可以将切除区域放置为在20％至50％范围内的并集值上具有交集。通过在切除中包括一些但不是压倒性的量的对象，切除可以因此创建更多的“具有挑战性”的示例，这些示例会部分遮挡对象而不会去除过多的相关图像数据。类似地，也可以基于图像中相机的预期视图来对图像的某些部分选择切除。例如，切除可以主要位于图像的下半部分或图像的中心，因为底部通常可以包括始终存在的伪像，而图像的中心可以是最感兴趣区域(例如，对于车辆，通常是车辆的行驶方向)。
用于切除区域的替换图像数据可以是纯色(例如，常数)，或者可以是另一图案，诸如高斯噪声。作为另一示例，为了表示遮挡或其他障碍物，切除可以替换为来自具有相同图像类型或标签的另一图像的图像数据补丁。最后，可以将切除与切除附近的区域混合，例如使用泊松混合。通过使用各种混合方法(诸如背景补丁或混合)，这些方法可以确保切除中的替换数据更难与环境区分开，并且从而提供与真实世界障碍物更为相似的示例。
尽管在图4中示出为矩形区域，但是在其他实施例中，在生成经增广的图像时应用的切除可以以不同形状变化。在生成经增广的图像410B、410C并且将经增广的图像与相关训练输出420B、420C相关联之后，图像增广模块330可以将图像添加到图像数据存储部350。
模型训练模块340基于由图像收集系统140捕获的图像和由图像增广模块330生成的经增广的图像来训练计算机模型。这些图像可以用作模型训练的图像训练集。在一个实施例中，机器学习模型是由模型训练模块340基于训练数据而训练的神经网络模型，诸如前馈网络、卷积神经网络(CNN)、深度神经网络(DNN)、递归神经网络(RNN)、自组织映射(SOM)等。在训练之后，计算机模型可以存储在已训练计算机模型存储部370中。模型接收传感器数据(例如，图像)作为输入，并且根据模型的训练来输出输出预测。在训练模型时，模型学习(或“训练”)一组参数，该组参数基于输入图像来预测输出，该输入图像是由训练数据的损失函数评估的。也就是说，在训练期间，根据当前该组参数来评估训练数据以生成预测。可以将针对训练输入的该预测与指定输出(例如，标签)进行比较，以评估损失(例如，具有损失函数)，并且可以通过优化算法来修改参数，以优化该组参数，从而减少损失函数。尽管被称为“优化”，但是这些算法可以减少相对于一组参数的损耗，但是可能无法在给定一组输入时保证找到参数的“最佳”值。例如，梯度下降优化算法可以找到局部最小值，而不是全局最小值。
通过在增广的训练数据上训练计算机模型，当将计算机模型应用于来自在具有捕获数据的传感器特性的环境中操作的物理传感器的传感器数据时，计算机模型可以以提高的精度来执行。由于增广保持了这些特性，因此这些传感器特性(例如，相机特性)在训练数据时所使用的图像中表示。在一个实施例中，训练数据不包括由图像操纵功能生成的经增广的图像，该图像操纵功能修改图像的相机属性，诸如裁剪、填充、翻转(垂直或水平)，或者向图像应用仿射变换(例如，剪切、旋转、平移、倾斜)。
在训练之后，模型分发模块380可以将已训练模型分配给系统以应用已训练模型。特别地，模型分发模块380可以将已训练模型(或其参数)发送给模型应用系统110，以用于基于模型应用系统110的传感器来检测图像的特性。因此，来自模型的预测可以用于模型应用系统110的操作，例如用于模型应用系统110的对象检测和控制。
为了说明的目的，已经给出了对本发明的实施例的前述描述；它并不旨在穷举或将本发明限制为所公开的精确形式。相关领域的技术人员可以理解，根据以上公开，很多修改和变化是可能的。
该描述的某些部分根据对信息的操作的算法和符号表示来描述本发明的实施例。这些算法的描述和表示通常由数据处理领域的技术人员用来将其工作的实质有效地传达给本领域其他技术人员。这些操作尽管在功能上、在计算上或在逻辑上进行描述，但应当理解为通过计算机程序或等效电路、微代码等来实现。此外，在不失一般性的情况下，有时将这些操作布置称为模块也是方便的。所描述的操作及其相关模块可以以软件、固件、硬件或其任何组合来体现。
本文中描述的任何步骤、操作或过程可以单独地或与其他设备组合地利用一个或多个硬件或软件模块来执行或实现。在一个实施例中，软件模块用计算机程序产品实现，该计算机程序产品包括包含计算机程序代码的计算机可读介质，该计算机程序代码可以由计算机处理器执行以用于执行所描述的任何或所有步骤、操作或过程。
本发明的实施例还可以涉及用于执行本文中的操作的装置(例如，系统)。该装置可以被具体地构造用于所需要的目的，和/或可以包括由存储在计算机中的计算机程序选择性地激活或重新配置的通用计算设备。计算设备可以是一个或多个处理器和/或计算机系统的系统或设备。这样的计算机程序可以存储在非暂态有形计算机可读存储介质中，或者存储在可以耦合到计算机系统总线的适合于存储电子指令的任何类型的介质中。此外，说明书中提到的任何计算系统可以包括单个处理器，或者可以是采用多个处理器设计以提高计算能力的架构。
本发明的实施例还可以涉及通过本文中描述的计算过程而生产的产品。这样的产品可以包括由计算过程产生的信息，其中该信息存储在非暂态有形计算机可读存储介质上，并且可以包括计算机程序产品的任何实施例或本文中描述的其他数据组合。
最后，本说明书中使用的语言主要是为了可读性和指导性目的而选择的，并且可能没有选择它来描绘或限制本发明的主题。因此，意图在于，本发明的范围不受该详细描述的限制，而是由基于此的在本申请上提出的任何权利要求来限制。因此，本发明的实施例的公开旨在说明而非限制本发明的范围，本发明的范围在所附权利要求中阐述。

Claims (20)

1.一种用于训练预测性计算机模型的一组参数的方法，所述方法包括：
获取多个图像和相关联的训练输出，所述图像由一个或多个车辆的相机捕获；
针对所述多个图像中的个体图像，基于使用保持所述个体图像的相机属性的一个或多个图像操纵功能中的图像操纵功能修改所述个体图像来生成针对所述个体图像的经增广的图像，使得与所述个体图像相关联的角度、比例和/或姿势被保留，其中所述经增广的训练图像与所述个体图像的所述相关联的训练输出相关联，
并且其中所述一个或多个图像操纵功能包括基于感兴趣区域来调整图像的部分的切除功能；以及
至少基于所述个体图像和所述经增广的图像来训练预测计算机模型，其中经训练的所述预测计算机模型被配置为预测输入图像中的对象的存在以用于特定车辆自治或半自治。
2.根据权利要求1所述的方法，其中所述感兴趣区域与行驶方向相对应。
3.根据权利要求1所述的方法，其中所述图像经由特定相机被获取，并且其中所述感兴趣区域与始终存在于经由所述特定相机被获取的图像中的伪像相对应。
4.根据权利要求1所述的方法，其中所述图像的所述部分与所述图像中的对象部分地重叠，所述切除是纯色并且所述切除与所述切除附近的区域混合。
5.根据权利要求1所述的方法，其中所调整的所述部分包括替换图像数据。
6.根据权利要求5所述的方法，其中所述替换图像数据是纯色或图案。
7.根据权利要求1所述的方法，其中所述一个或多个车辆和所述特定车辆具有至少一个相机，所述至少一个相机具有相同配置。
8.根据权利要求7所述的方法，其中所述相同配置指示关于所述一个或多个车辆和特定车辆的相同位置和/或取向。
9.一种系统，包括一个或多个处理器、以及存储指令的非暂态的计算机存储介质，所述指令在由所述一个或多个处理器执行时使所述处理器：
获取多个图像和相关联的训练输出，所述图像由一个或多个车辆的相机捕获；
针对所述多个图像中的个体图像，基于使用保持所述个体图像的相机属性的一个或多个图像操纵功能中的图像操纵功能修改所述个体图像来生成针对所述个体图像的经增广的图像，使得与所述个体图像相关联的角度、比例和/或姿势被保留，其中所述经增广的训练图像与所述个体图像的所述相关联的训练输出相关联，
并且其中所述一个或多个图像操纵功能包括基于感兴趣区域来调整图像的部分的切除功能；以及
至少基于所述个体图像和所述经增广的图像来训练预测计算机模型，其中经训练的所述预测计算机模型被配置为预测输入图像中的对象的存在以用于特定车辆自治或半自治。
10.根据权利要求9所述的系统，其中所述感兴趣区域与行驶方向相对应。
11.根据权利要求9所述的系统，其中所述图像经由特定相机被获取，并且其中所述感兴趣区域与始终存在于经由所述特定相机被获取的图像中的伪像相对应。
12.根据权利要求9所述的系统，其中所述图像的所述部分与所述图像中的对象部分地重叠，所述切除是纯色并且所述切除与所述切除附近的区域混合。
13.根据权利要求9所述的系统，其中所调整的所述部分包括替换图像数据，所述替换图像数据包括纯色或图案。
14.根据权利要求9所述的系统，其中所述一个或多个车辆和所述特定车辆具有至少一个相机，所述至少一个相机具有相同配置。
15.根据权利要求14所述的系统，其中所述相同配置指示关于所述一个或多个车辆和特定车辆的相同位置和/或取向。
16.一种非暂态的计算机可读介质，具有用于由处理器执行的指令，所述指令在由所述处理器执行时使所述处理器：
获取多个图像和相关联的训练输出，所述图像由一个或多个车辆的相机捕获；
针对所述多个图像中的个体图像，基于使用保持所述个体图像的相机属性的一个或多个图像操纵功能中的图像操纵功能修改所述个体图像来生成针对所述个体图像的经增广的图像，使得与所述个体图像相关联的角度、比例和/或姿势被保留，其中所述经增广的训练图像与所述个体图像的所述相关联的训练输出相关联，
并且其中所述一个或多个图像操纵功能包括基于感兴趣区域来调整图像的部分的切除功能；以及
至少基于所述个体图像和所述经增广的图像来训练预测计算机模型，其中经训练的所述预测计算机模型被配置为预测输入图像中的对象的存在以用于特定车辆自治或半自治。
17.根据权利要求16所述的计算机可读介质，其中所述感兴趣区域与行驶方向相对应。
18.根据权利要求16所述的计算机可读介质，其中所述图像经由特定相机被获取，并且其中所述感兴趣区域与始终存在于经由所述特定相机被获取的图像中的伪像相对应。
19.根据权利要求16所述的计算机可读介质，其中所述图像的所述部分与所述图像中的对象部分地重叠，所述切除是纯色并且所述切除与所述切除附近的区域混合。
20.根据权利要求16所述的计算机可读介质，其中所调整的所述部分包括替换图像数据，所述替换图像数据包括纯色或图案。
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