CN113392693A - 不同照明条件下的对象检测 - Google Patents

不同照明条件下的对象检测 Download PDF

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CN113392693A
CN113392693A CN202110215098.2A CN202110215098A CN113392693A CN 113392693 A CN113392693 A CN 113392693A CN 202110215098 A CN202110215098 A CN 202110215098A CN 113392693 A CN113392693 A CN 113392693A
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伊戈尔·雷切尔高兹
卡琳娜·奥迪纳耶夫
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Abstract

本申请公开了不同照明条件下的对象检测。一种用于在不同照明条件下进行图像处理的方法，该方法可以包括获取车辆环境的图像；选择位于图像上部的感兴趣区域内的一组像素；基于该组像素的值计算照明条件指标；基于照明条件指标，从机器学习过程中选择所选的机器学习过程；其中不同的机器学习过程针对不同照明条件进行训练；以及通过所选的机器学习过程处理图像以提供处理结果。

Description

不同照明条件下的对象检测
交叉引用
本申请要求提交日期为2020年2月25日的序列号为16/799,863的美国临时专利申请的优先权。该临时专利申请通过引用并入本文。
技术领域
本公开大体上涉及对象检测。
背景
机器学习过程可能需要在大范围的照明条件下操作。一种照明条件与另一种照明条件之间的巨大差异降低了使用单个机器学习过程来处理在大范围的不同照明条件下获取的图像的效率。
例如，使用单个机器学习过程在日间光线条件和夜间条件(低照度或无照度)下执行图像处理将导致次优的机器学习过程图像处理。
附图简述
根据以下结合附图的详细描述将更全面地理解和认识本公开的实施例，在附图中：
图1示出了方法；
图2-11示出了图像和感兴趣区域；以及
图12示出了装置。
示例实施方式的描述
说明书和/或附图可以涉及图像。图像是媒体单元的示例。对图像的任何引用可以经过必要修改应用于媒体单元。媒体单元可以是感测信息的示例。对媒体单元的任何引用可以经过必要修改应用于自然信号，例如但不限于由自然生成的信号、代表人类行为的信号、代表与股票市场相关的操作的信号、医疗信号等。对媒体单元的任何引用可以经过必要修改应用于感测信息。感测信息可以由任何类型的传感器感测，例如可见光照相机，或者可以感测红外线、雷达成像、超声波、电光、放射影像、激光雷达(光检测和测距)等的传感器。
说明书和/或附图可以涉及处理器。处理器可以是处理电路系统。处理电路系统可以实现为中央处理单元(CPU)和/或一个或更多个其他集成电路，例如专用集成电路(ASIC)、现场可编程门阵列(FPGA)、全定制集成电路等或这些集成电路的组合。
可以提供说明书和/或附图中示出的任何方法的任何步骤的任何组合。
可以提供任何权利要求的任何主题的任何组合。
可以提供说明书和/或附图中示出的系统、单元、部件、处理器、传感器的任何组合。
不同照明条件下的图像处理
提供了一种方法、系统和非暂时性计算机可读介质，其将不同的机器学习过程分配给不同的照明条件(通常是每个照明条件的子范围一个机器学习过程)。基于估计进行了选择。
图1示出了在不同照明条件下的图像处理的方法9100的示例。
方法9100可以由初始化步骤9110开始。
初始化步骤9110可以包括针对不同照明条件训练机器学习过程，或者接收针对不同照明条件进行训练的机器学习过程。
训练可以是监督训练和/或无监督训练。
机器学习过程可以是夜间特点机器学习过程(night flavor machine learningprocess)和日间特点机器学习过程(day flavor machine learning process)。
可以有两个以上的不同的机器学习过程。
每个机器学习过程可以覆盖照明条件的子范围，该子范围可以部分重叠或者可以根本不重叠。
可以训练机器学习过程来在不同照明条件下理解车辆环境。
对环境的了解可以包括例如对象检测、车道检测、标志识别、对象的过程的估计等。
步骤9110后面可以是获取车辆环境的图像的步骤9120。
车辆环境可以是在车辆的一个或更多个传感器的一个或更多个视场内的区域。
步骤9120之后可以是步骤9130，步骤9130选择位于图像上部的感兴趣区域内的一组像素。
该组像素可以由图像的全部像素的总数的一部分(例如，一小部分)形成。该小部分可以小于图像中像素总数的20％、10％、5％、1％、0.5％、0.1％、0.05％、0.001％、0.0005％。
可以选择该组像素的任何特征(位置、感兴趣区域内的分布、数量)，以在(a)计算照明条件指标(illumination condition indicator)的简单性和/或速度(尽可能少的像素)和(b)照明条件指标的准确性(更多的像素)之间提供期望折衷。期望折衷可以由人工、半自动地、自动地、以固定方式来确定，可以动态地改变，可以基于用于执行照明条件的计算的可用资源，可以取决于不同机器学习过程的数量等。
感兴趣区域可以具有任何形状和/或大小。
感兴趣区域的任何特征(大小、形状、取向和位置)可以被预先确定、固定或动态更新。
例如，感兴趣区域可以是多边形(例如，倒三角形、倒梯形、具有基本上沿着图像的整个宽度延伸的或者仅沿着图像的一部分宽度延伸的上底部的倒梯形)，可以是弯曲的，可以包括线性和非线性段的组合等等。
对于感兴趣区域的动态定义的另一个示例，假设提供了连续的帧，感兴趣区域可以根据先前帧中的消失点的估计而改变(感兴趣区域必须在该点之上)。该修改还可以考虑对先前帧内的对象边界框的估计(感兴趣区域不应该包括这些对象中的任何一个)。另外，可以针对由消失点和对象位置的估计所定义的边界内的少数可能形状来计算照明条件指标，并且选择具有最低条件照明指标的感兴趣区域作为我们的感兴趣区域。
消失点是透视图的图像平面上的一个点，在该点上，三维空间中相互平行线的二维透视投影(或图形)看起来会聚。当一组平行线垂直于一个图像平面时，这种结构被称为单点透视，并且它们的消失点对应于眼睛，或“眼点”，从该点应该看到图像的正确的透视几何图形。传统的线性图使用具有定义一到三个消失点的一到三组平行线的对象。(www.wikipedia.org)
步骤9130之后可以是基于该组像素的值计算照明条件指标的步骤9140。
计算可以包括应用任何函数，例如平均值、加权平均值、强度范围、异常值剔除等。
步骤9140之后可以是步骤9150，步骤9150基于照明条件指标从机器学习过程中选择所选的机器学习过程；其中不同的机器学习过程针对不同照明条件进行训练。
机器学习过程可以由一个或更多个处理电路系统来执行。
未被选择的机器学习过程可以保持在空闲模式，可以被停用，或者可以被忽略。
该图像可以仅提供给所选的机器学习过程。
步骤9150之后可以是步骤9160，步骤9160通过所选的机器学习过程处理图像以提供处理结果。
步骤9160可以包括了解车辆的环境。对环境的了解可以包括例如对象检测、车道检测、标志识别、对象的过程的估计等。
步骤9160之后可以是对处理的成果做出响应的步骤9170。
步骤9170可以包括执行自主驾驶操作。
步骤9170可以包括执行自主驾驶操作(其中车辆控制驾驶的一个方面(例如，车道校正、应急操纵)，而人类驾驶员控制其他方面)。
步骤9170可以包括在不控制车辆的情况下辅助驾驶员。
步骤9170可以包括与自主驾驶模块通信。步骤9170可以包括与部分自主驾驶模块通信。
步骤9170可以包括与驾驶员辅助模块通信。
该通信可以包括发送处理的成果、触发操作、发送中断等。
步骤9170可以包括基于步骤9160的成果动态更新像素数量和/或像素位置。
例如，图像处理的质量可以以一种或更多种方式评估(例如，检查对象检测的准确性等)，并且当不准确时，可以修改感兴趣区域和/或该组像素的任何特征(位置、数量)。图像处理的质量评估可以按每组图像、按每个周期或时间等来执行。单个质量评估可以考虑在一个或更多个周期期间获取的多个图像。
对象处理可以更好地检测照明条件(例如，找到图像中天空的准确位置，并且当处理接下来的图像之一时，感兴趣区域和/或该组像素的位置和/或数量可以被选择为落在天空上和/或不落在天空之外。
步骤9170可以包括估计天空在特定图像中的位置，并且基于(a)天空在该特定图像中的估计位置和(b)在该特定图像的获取时间和未来图像的视场中的估计变化之间获取一个或更多个特定图像的照相机的视场中的估计变化来估计天空在未来图像中的位置。
一旦估计了变化，则可以通过补偿FOV的估计变化来确定天空在未来图像中的预期位置。例如，如果估计的变化是某个方向上的X度变化，则天空将位于可在未来图像中与相反方向呈X度被定位的新位置。角度变化被转化为未来图像的像素的变化，这些像素预计将覆盖天空。
步骤9170可以包括接收或生成天空的位置的粗略估计，并确定感兴趣区域落在天空位置的粗略估计内。
执行步骤9130、9140和9150的总持续时间可以是处理图像所需的周期的一小部分。例如，假设获取速率为每秒50张图片，总持续时间可以是20毫秒的一小部分(例如，小于20％、10％、5％、1％)。
图2-11示出了不同的图像和位于图像上部的不同感兴趣区域。
感兴趣区域可以具有任何形状、大小和/或取向。感兴趣区域可以关于图像中心(垂直轴)对称，或者也可以不对称。
图2示出了环形交叉路口的图像3080。图像3080包括环形交叉路口内的弯曲道路3072、围绕环形交叉路口的内圆3074的环形区3073、沙子3078和位于内圆3074中的少量灌木或植物3075、围绕环形交叉路口的各种树木3076、围绕环形交叉路口的人行道3075以及即将进入环形交叉路口的车辆3071。
在图像3080内定义了感兴趣区域9000。感兴趣区域是倒三角形，其底部横跨图像3080的整个宽度。该组像素是密集填充的像素网格9020。
图3示出了图像3808。感兴趣区域9001是倒梯形，并且该组像素是密集填充的像素网格9020。倒梯形的底部横跨图像3080的整个宽度。
图4示出了图像3808。感兴趣区域9002是横跨图像3808的整个长度的矩形，并且该组像素是密集填充的像素网格9020。
图5示出了图像3808。感兴趣区域9003是集中在图像中心的矩形，并且该组像素是密集填充的像素网格9020。
图6示出了图像3080。感兴趣区域9000是倒三角形，其底部横跨图像3080的整个宽度。该组像素是不太密集填充的像素网格9020。
图7示出了图像3808。感兴趣区域9004是倒梯形，并且该组像素是密集填充的像素网格9020。倒梯形的底部横跨图像3080的整个宽度的一部分。
图8示出了图像3808。感兴趣区域9005是弯曲的，并且该组像素是密集填充的像素网格9020。
图9示出了图像3808。感兴趣区域9006是关于图像的(由垂直轴9020表示的)中心不对称的倒梯形。该组像素是密集填充的像素网格9020。
图10示出了图像1040。感兴趣区域9007是倒三角形，其底部横跨图像1040的整个宽度。该组像素是不太密集填充的像素网格9020。
图11示出了图像1040。感兴趣区域9008是倒梯形，其底部横跨图像1040的整个宽度。该组像素是不太密集填充的像素网格9020。
图12示出了装置9140。
装置被配置成执行方法9100。
装置9100可以包括图像获取单元9141、选择单元9142、至少两个(Q个)机器学习过程9143(1)–9143(Q)和附加单元9144。
图像获取单元9141被配置为获取车辆环境的图像。图像获取单元9141可以包括图像传感器，或者可以被配置为从传感器接收图像(直接地或间接地)。
该图像可以是由传感器感测的原始图像或经处理的图像。例如，图像可以是平滑图像、滤波图像、降噪图像等。
选择单元9154可以被配置成基于该组像素的值来计算照明条件指标，并且基于照明条件来选择所选的机器学习过程，例如，找到最适合照明条件的机器学习过程，该机器学习过程针对照明条件的子范围进行训练，该子范围包括由照明条件指标指示的照明条件。
一旦选择单元选择了所选的机器学习过程(9143(q)，q的范围在1和Q之间)，则第q个机器学习过程被馈送图像，并且通过所选的机器学习过程对图像执行处理以提供处理结果。
处理结果被馈送到附加单元9144。
附加单元9144可以执行响应处理结果的步骤9170。
附加单元9144可以是处理电路系统，可以是通信模块，可以是警报发生器，等等。
附加单元9144可以与各种模块通信，例如自主驾驶模块、部分自主驾驶模块(例如车道校正模块)、驾驶员辅助模块等。
说明书中对方法的任何引用应当经必要修改而应用于能够执行该方法的系统，并且应当经必要修改而应用于非暂时性计算机可读介质，该非暂时性计算机可读介质可以存储一旦由计算机执行就导致执行方法的指令。
说明书中对系统和任何其他部件的任何引用应当经必要修改而应用于可以由系统执行的方法，并且应当经必要修改而应用于可以存储可由系统执行的指令的非暂时性计算机可读介质。
说明书中对非暂时性计算机可读介质的任何引用应当经必要修改而应用于能够执行存储在非暂时性计算机可读介质中的指令的系统，并且应当经适当修改而应用于可以由计算机执行的方法，该计算机读取存储在非暂时性计算机可读介质中的指令。
可以提供任何附图、说明书的任何部分和/或任何权利要求中列出的任何模块或单元的任何组合。特别是可以提供任何要求保护的特征的任何组合。
对术语“包括”或“具有”的任何引用也应被解释为是指“由...组成”或“基本由...组成”。例如，分别地，包括特定步骤的方法可以包括额外的步骤，可以限于特定步骤，或者可以包括不会对该方法的基本和新颖特征产生实质性影响的额外步骤。
本发明也可以在用于在计算机系统上运行的计算机程序中实施，该计算机程序至少包括用于在可编程装置(例如计算机系统)上运行时执行根据本发明的方法的步骤、或者使得可编程装置能够执行根据本发明的设备或系统的功能的代码部分。计算机程序可以使存储系统将硬盘驱动器分配给硬盘驱动器组。
计算机程序是例如特定应用程序和/或操作系统的指令列表。计算机程序可以例如包括以下各项中的一个或更多个：子例程、函数、过程、对象方法、对象实现、可执行应用、小应用程序、小服务程序、源代码、目标代码、共享库/动态加载库和/或被设计用于在计算机系统上执行的其它指令序列。
计算机程序可以在内部存储在诸如非暂时性计算机可读介质的计算机程序产品上。所有或一些的计算机程序可以设置在永久地、可移除地或远程地耦合到信息处理系统的非暂时性计算机可读介质上。非暂时性计算机可读介质可以包括例如但不限于任何数量的以下各项：磁存储介质，包括硬盘和磁带存储介质；光存储介质，诸如光盘介质(例如，CD-ROM、CD-R等)和数字视频盘存储介质；非易失性存储器存储介质，包括基于半导体的存储器单元，诸如闪存存储器、EEPROM、EPROM、ROM；铁磁数字存储器；MRAM；易失性存储介质，包括寄存器、缓冲器或缓存、主存储器、RAM等。计算机进程通常包括执行(运行)程序或程序的一部分、当前程序值和状态信息以及由操作系统使用以管理进程的执行的资源。操作系统(OS)是管理计算机资源共享、并为程序员提供用于访问这些资源的接口的软件。操作系统处理系统数据和用户输入，并通过分配和管理作为对系统的用户和程序的服务的任务和内部系统资源进行响应。计算机系统可以例如包括至少一个处理单元、相关联的存储器和多个输入/输出(I/O)设备。当执行计算机程序时，计算机系统根据计算机程序处理信息，并且经由I/O设备产生所得到的输出信息。
在前述说明书中，已经参考本发明的实施例的具体示例描述了本发明。然而，显然，在不脱离如所附权利要求中阐述的本发明的更广泛的精神和范围的情况下，可以在其中进行各种修改和改变。
此外，在说明书中和权利要求中的术语“前”、“后”、“顶部”、“底部”、“在...之上”、“在...之下”等(如果有的话)用于描述性目的，而不一定用于描述永久相对位置。应当理解，这样使用的术语在适当的情况下是可互换的，使得本文所描述的本发明的实施例例如能够在除了本文所示出或另外描述的那些方向之外的其它方向上操作。
本领域技术人员将认识到，逻辑块之间的边界仅仅是说明性的，并且可选实施例可以合并逻辑块或电路元件，或者对各种逻辑块或电路元件施加功能的替代分解。因此，应当理解，本文所描述的体系结构仅仅是示例性的，并且实际上可以实施实现相同功能的许多其它体系结构。
实现相同功能的部件的任何布置被有效地“关联”，使得实现期望的功能。因此，本文组合以实现特定功能的任何两个部件可以被看作彼此“相关联”，使得实现期望的功能，而与体系结构或中间部件无关。同样，这样关联的任何两个部件也可以被视为彼此“可操作地连接”或“可操作地耦合”以实现期望的功能。
此外，本领域技术人员将认识到上述操作之间的边界仅是说明性的。多个操作可以组合成单个操作，单个操作可以分布在附加操作中，并且操作可以在时间上至少部分地重叠地执行。此外，可选实施例可以包括特定操作的多个实例，并且在各种其它实施例中可以改变操作的顺序。还例如，在一个实施例中，所示示例可以被实施为位于单个集成电路上或在同一设备内的电路。另选地，该示例可实施为以合适方式彼此互连的任何数目的单独集成电路或单独设备。
还例如，示例或其部分可以实施为物理电路的或者可转换成物理电路的逻辑表示的软或代码表示，例如以任何适当类型的硬件描述语言实施。
此外，本发明不限于在非可编程硬件中实现的物理设备或单元，但也还可以应用在能够通过根据适当的程序代码进行操作来执行期望的设备功能的可编程设备或单元，诸如大型机、小型计算机、服务器、工作站、个人计算机、记事本、个人数字助理、电子游戏、汽车和其他嵌入式系统、蜂窝电话和在本申请中通常被表示为“计算机系统”的各种其他无线设备中。
然而，其它修改、变化和替代也是可能的。因此，说明书和附图被认为是说明性的而不是限制性的。
在权利要求中，置于括号之间的任何附图标记不应被解释为限制权利要求。词“包括”不排除除了在权利要求中列出的那些之外的其他元件或步骤的存在。此外，如本文所使用的术语“一(a)”或“一(an)”被定义为一个或多于一个。此外，在权利要求中的引导性短语(例如“至少一个”和“一个或更多个”)的使用不应被解释为暗示由不定冠词“a”或“an”引入另一个权利要求要素将包含这样引入的权利要求要素的任何特定权利要求限制到仅包含一个这样的要素的发明，即使同一权利要求包括引导性短语“一个或更多个”或“至少一个”和不定冠词(例如“a”或“an”)。同理适用于定冠词的使用。除非另有说明，否则诸如“第一”和“第二”的术语用于任意地区分开这样的术语所描述的要素。因此，这些术语不一定旨在指示这样的要素的时间或其他优先级。某些度量在相互不同的权利要求中被叙述的不争事实并不指示这些度量的组合不能有利地被使用。
虽然本文已经图示和描述了本发明的某些特征，但是本领域普通技术人员将想到许多修改、替换、改变和等同物。因此，应当理解，所附权利要求旨在覆盖落入本发明的真实精神内的所有这样的修改和改变。

Claims (18)

1.一种用于在不同照明条件下进行图像处理的方法，所述方法包括：
获取车辆的环境的图像；
选择位于所述图像上部的感兴趣区域内的一组像素；
基于所述一组像素的值计算照明条件指标；
基于所述照明条件指标，从机器学习过程中选择所选的机器学习过程；其中不同的机器学习过程针对不同照明条件进行训练；和
通过所述所选的机器学习过程处理所述图像以提供处理结果。
2.根据权利要求1所述的方法，其中，所述一组像素由少于所述图像的总像素的百分之一的像素形成。
3.根据权利要求1所述的方法，其中，所述感兴趣区域是倒三角形。
4.根据权利要求1所述的方法，其中，所述感兴趣区域是倒梯形。
5.根据权利要求1所述的方法，其中，所述感兴趣区域是倒三角形或倒梯形，所述倒三角形或倒梯形具有基本上沿着所述图像的整个宽度延伸的上底部。
6.根据权利要求1所述的方法，包括接收或生成天空的位置的粗略估计，并确定所述感兴趣区域落在天空的位置的粗略估计内。
7.根据权利要求1所述的方法，其中，所述一组像素的位置被预先定义。
8.根据权利要求1所述的方法，其中，所述一组像素的选择、所述照明条件指标的计算以及所述所选的机器学习过程的选择是实时执行的。
9.根据权利要求1所述的方法，其中，所述机器学习过程是夜间特点机器学习过程和日间特点机器学习过程。
10.根据权利要求1所述的方法，其中，所述一组像素是间隔开的像素的网格。
11.根据权利要求1所述的方法，其中，所述机器学习过程被训练以了解车辆的环境。
12.根据权利要求1所述的方法，其中，所述处理包括对象检测。
13.根据权利要求1所述的方法，其中，所述处理包括车道检测。
14.根据权利要求1所述的方法，其中，所述处理包括标志识别。
15.根据权利要求1所述的方法，其中，所述处理包括所述环境中对象的未来运动的预测。
16.根据权利要求1所述的方法，其中，所述处理包括执行至少部分自主驾驶。
17.一种存储指令的非暂时性计算机可读介质，所述指令用于：
获取车辆的环境的图像；
选择位于所述图像上部的感兴趣区域内的一组像素；
基于所述一组像素的值计算照明条件指标；
基于所述照明条件指标，从机器学习过程中选择所选的机器学习过程；其中不同的机器学习过程针对不同照明条件进行训练；和
通过所述所选的机器学习过程处理所述图像以提供处理结果。
18.一种装置，其包括至少一个单元，所述单元被配置为
获取车辆的环境的图像；
选择位于所述图像上部的感兴趣区域内的一组像素；
基于所述一组像素的值计算照明条件指标；
基于所述照明条件指标，从机器学习过程中选择所选的机器学习过程；其中不同的机器学习过程针对不同照明条件进行训练；和
通过所述所选的机器学习过程处理所述图像以提供处理结果。
CN202110215098.2A 2018-10-18 2021-02-25 不同照明条件下的对象检测 Pending CN113392693A (zh)

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