CN110378184A - 一种应用于自动驾驶车辆的图像处理方法、装置 - Google Patents
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Abstract
本申请公开了一种应用于自动驾驶车辆的图像处理方法、装置。一方面,该方法包括接收来自于一个传感器阵列的图像;其中,该接收到的图像包括x‑y像素阵列,并且基于一个图谱中的对应的x‑y值、所述x‑y像素阵列中的每个像素具有从三原色中选择出的一个颜色的值。该方法还包括对接收到的图像进行预处理得到预处理图像。该方法还包括对预处理图像进行机器感知处理,确定得到图像中多个物理实体的一个或多个轮廓。
Description
技术领域
本申请涉及图像处理领域,特别涉及一种应用于自动驾驶车辆的图像处理方法、装置和存储介质。
背景技术
自动驾驶是一种感知车辆的位置和运动,并根据感知的结果为车辆导航、控制车辆向目的地行驶的技术。自动驾驶技术在人们出行、货物运输和运输服务方面具有重要的应用。自动驾驶能够保证车辆和乘客的安全,以及车辆附近的人和物的安全。自动驾驶技术的一个组成部分包括对车辆摄像头获取的图像进行分析,以识别出自动驾驶车辆所在道路上的固定物体或者移动物体。
发明内容
本申请实施例提供了一种应用于自动驾驶车辆的图像处理方法、装置和存储介质。一方面,该方法包括图像处理装置接收来自于一个传感器阵列的图像;其中,该接收到的图像包括二维像素矩阵,所述像素矩阵中的每个像素具有根据一个预设图谱中对应像素的颜色、从该像素的三原色中选择出的对应的颜色的值,该预设图谱中的每个像素具有三原色中的一种颜色。该方法还包括对接收到的图像进行预处理得到预处理图像。该方法还包括对预处理图像进行感知处理,确定得到物体识别的结果。
该方法还可以包括以下特征的任意结合。所述接收到的图像是通过与所述图谱对应的一个或多个滤色片、对所述传感器阵列获得的原始图像进行滤色生成的。在所述图谱中包括的绿色像素值的数量多于红色像素值的数量和蓝色像素值的数量。在根据所述图谱选择后的图像中,在一行像素中或在一列像素中,每间隔一个像素的颜色为绿色。所述图谱为拜尔图谱,所述拜尔图谱具有重复排列的2*2像素网格,每个像素网格中的四个像素的颜色分别为红-绿-绿-蓝。所述预处理包括图像裁剪处理、图像缩放处理、和/或图像压缩处理。所述预处理中去除掉去马赛克处理、白平衡处理和/或去噪处理。物体识别的结果包括识别出图像中多个物理实体中的一个或者多个的轮廓。物体识别的结果包括在图像中的物体上叠加边界框。所述传感器阵列包括摄像头。
另一方面,本申请实施例提供的图像处理装置,包括一个处理器、一个存储器以及一个通信接口,其中该处理具有内置程序,该程序被执行后实现如上所述的图像处理方法。
另一方面,本申请实施例提供的计算机可读存储介质中存储有代码,该代码被一个处理器执行后实现如上所述的图像处理方法。
上述方面和公开的特征将在以下的附图和说明书中进一步描述。
附图说明
附图用来提供对本发明的进一步理解,并且构成说明书的一部分,与本发明的实施例一起用于解释本发明,并不构成对本发明的限制。
图1为本申请一些实施例提供的应用于自动驾驶车辆的图像处理方法的处理流程图;
图2为本申请提供的应用于自动驾驶车辆的减少彩色图像的数据流的示例;
图3为通过不同比例的拜尔图像来显示拜尔图谱的示例;
图3A为显示为灰度色阶的拜尔图谱图像的一个示例;
图4为本申请一些示例性实施例提供的感知结果的示例;
图5为本申请一些实施例提供的装置的示例。
具体实施方式
静态摄像头或者视频摄像头获取的图像是为人类视觉所用的。全彩色图像在三原色上具有高分辨率,三原色包括红色(R)、绿色(G)和蓝色(B)。图像处理通常对处理过的和渲染好的图像进行处理,这些图像通常是针对人类视觉的,这些处理包括多种任务,例如物体识别、语义分割以及其它任务。但是,用于机器处理的图像不需要具有用于人类视觉处理的图像所具有的特征。例如,应用于自动驾驶车辆的机器视觉处理可以不包括针对人类视觉的图像处理,例如去马赛克、白平衡、色彩还原等处理。由于这些处理不会为图像增加新的信息,也不会为机器视觉处理带来增益,从而不是机器视觉处理所需或者有用的处理。如果不执行一些处理也可以得到机器视觉所需的图像,例如引起过度曝光的白平衡。而且,机器视觉处理所需的表达图像的数据量,要少于通过全彩色RGB表达图像的数据量。其中,去马赛克处理包括在传感器阵列上叠加一个滤色阵列,从传感器阵列输出的不完整颜色样本中重建出全彩色图像。白平衡处理包括缩放一个或多个像素的R,G或B值。去噪处理包括去除椒盐噪声,在椒盐噪声中一个噪声像素与周围像素的颜色不具有关联;或者去噪处理包括去除高斯噪声。
在一些示例性实施例中,可以减少图像中每个像素的RGB信息、使每个像素只具有R、G或B中的一个颜色的强度值,而不是每个像素都包括全部R、G和B的强度值,这样可以减少图像中每个像素的RGB信息。可以针对一个图像中的像素阵列,预设一个RGB图谱。通过将每个像素的强度值的数量从三个减少到一个,表达一个图像的数据量能够减少到一帧全彩色RGB图像数据量的三分之一。通过这种方式可以将表达一个图像的数据量减少到三分之一,同时能够维持图像中具有色彩的物体所必需的色彩敏感度。并且通过减少表达图像的数据量,能够减少在指定时间内传输图像的带宽,或者能够在更少的时间内传输图像或处理图像。这些特征都能够改进机器视觉的性能和响应能力。例如,一个摄像头获取的图像具有200*300个像素,也即60,000个像素,如果通过一个8比特的红色值、一个8比特的绿色值和一个8比特的蓝色值来表达每个像素,那么表达一个彩色图像的总数据量为300(像素)*200(像素)*8(位)*3(颜色)=1,440,000位比特。通过使用预设图谱为每个像素从R,G和B三个颜色中选择一个颜色,表达图像的数据的比特量能够减少到480,000比特。以上只是通过一个例子进行了说明,其它的图像像素数量、每种颜色分辨率的比特位,不做具体限定。
本申请实施例提供的图像处理方法可以通过一个图像处理装置来执行。该装置可以位于自动驾驶车辆中,例如自动驾驶车辆的车载服务器。该装置也可以是一个用于模拟或者测试自动驾驶功能的计算装置,该装置可以是云端计算装置或者本地计算装置,在以下的描述中该也被称为计算装置。
图1示出了本申请一些实施例提供的应用于自动驾驶车辆的图像处理流程100。在步骤110中,接收一个图像。在步骤130中,对图像进行预处理。在步骤140中,对预处理后的图像进行感知处理。步骤150中,输出感知处理结果。
在步骤110中,接收来自一个摄像头的图像。例如,图像处理装置接收来自于一个固态摄像头例如电荷耦合设备(CCD)的图像。该图像(或称为原始图像)包括二维像素矩阵,或者称为x-y像素矩阵。该摄像头可以分别输出R、G和B的数据,或者输出一个合成RGB的数据。例如,R、G和B中的每一个可以被表示为一个8比特的亮度值,或者表示为模拟电压值。如果每个像素只有一个对应于R、G或者B的强度值,而不是每个像素具有与R、G和B的对应的三个强度值,就可以用较少的数据量来表达一个图像。通过将强度值从三个减少到一个,表达一个图像的数据量减少到表达一个全彩色RGB图像的数据量的三分之一。通过这种方式,表达一个图像的所需的数据量可以减少到原有的三分之一,并且能够维持图像中具有色彩的物体所需的色彩敏感度。具体可以通过一个图谱为每个像素从三原色中选择一个颜色,例如通过拜尔图谱(Bayer Pattern)来选择,以下将通过图3来具体说明。
在步骤130中,对接收到的图像进行预处理。预处理包括最必要的处理,以提高处理速度、降低计算复杂度。例如,预处理中可以不包括去马赛克处理、白平衡处理和/或降噪处理,也即可以在预处理包括的多个处理中去掉去马赛克处理、白平衡处理和/或降噪处理,预处理包括的多个处理中可以包括基本的诸如图像裁剪、缩放、和/或压缩的基本处理。
在步骤140中,对预处理后的图像进行感知处理。感知处理的结果包括在物理实体上叠加边界框,或者识别出图像中多个物理实体中的一个或者多个的轮廓。如图4中的420所示,由于输入图像中每个像素不包括全部的RGB三个通道的色彩,只包括RGB中一个通道的色彩,使得感知操作得到了改变,可以实现在一辆移动车辆中进行实时速度的感知计算,且不需要人工辅助或者反馈。
在步骤150中,输出感知操作的结果。该输出结果可以更进一步地用于物体识别和车辆控制相关的图像处理任务。
本申请实施例提供的技术方案的优点包括:通过为图像生成一个色彩通道,而不是如通常的为图像生成三个色彩通道(RGB),可以将图像所需的存储空间减少三分之二,减少数据传输的数据率,或者综合减少数据传输量和传输时间。第二个优点在于,由于图像的数据量减少了,所需处理的数据量减少了,一些预处理步骤能够被省略掉,从而能够减少计算需求。另一个优点在于,由于预处理的一些处理被省略了(例如去掉了可能引起过度曝光或者欠曝光的白平衡),使得预处理后的图像数据可以包含更多的信息(即使原始数据的数据量减少了),这样能够提升图像处理的性能。
图2中的210示出了一个图谱的示例。全彩色图像包括三个通道——RGB。一个图像中的每个像素都具有三个对应的表达色彩的值。这三个色彩的混合得到数字图像中的一个“色点”。如上所述,可以为每个像素从三个色彩中选择一个色彩,而不是三个色彩的值。具体地可以通过一个图谱来为每个像素选择一个色彩。例如,可以通过拜尔图谱(BayerPattern)来选择。拜尔图谱中的每个像素具有一个通道,并且通过该一个通道的编码得到RGB三个通道的信息的子集。例如,可以选择一个具有重复排列的2*2像素网格的图谱,在该图谱中,每个像素具有一个通道值。在一些实施例中,可以使用“RGGB”的拜尔图谱。如图2中210所示,RGGB指在一个重复的矩形4像素模板内包括像素211的红色,像素212的绿色,像素213的绿色,以及像素214的蓝色。在本申请的实施例中,可以根据硬件来选择不同的图谱,例如,通过与一个图谱对应的一个或多个滤色片,对传感器阵列获取的原始图像进行滤色,生成选择后的图像,也即图1中步骤110中接收到的图像。通过这种方式,在像素211的对应于R、G和B的三个值中,选择R的值,而G的值和B的值被丢弃。对于每个像素均可以执行这种选择处理。拜尔图谱中包括的绿色的值的数量多于红色的值的数量和蓝色的值的数量。在通过图像选择后的图像中,在一行像素中,每间隔一个像素的像素值对应于原始图像中该像素的绿色值,或者在一列像素中,每间隔一个像素的像素值对应于原始图像中该像素的绿色值。该图谱中包括的像素的绿色的值多于红色和蓝色的值。相类似地,图谱中红色的值或者蓝色的值也可以多于绿色的值。
图2中的220示出了对一个示例像素从三个颜色值中选择一个值的处理,其中每个马赛克图对应于从一个图像中选择出的三个颜色中的一个颜色。生成通过图谱选择的图像的操作,可以使用一个或多个滤色片来实现。该多个滤色片可以是一个滤色片阵列(ColorFilter Array,CFA)。
图3中的305和310示出了通过一个RGGB的拜尔图谱对一个全彩色图像进行滤色的示例。在310的图像中,该图谱被放大显示,以更好地显示该图谱。310的图像与305的图像、320的图像以及图4中的图像都不同。305中的图像具有RGGB拜尔图谱,该图像与图3A的图像是同一个图像,而图3A图像中的每一个像素只具有R、G或B中的一个色彩的值。图3A中的320示出了一个具有拜尔图谱的图像,该图谱具有R、G和B的值,且该图谱映射为一个黑白的灰阶图像。
图4的410示出了在320图像上执行图像处理后所呈现的结果。图4中420示出了感知处理的结果,该结果显示了在具有拜尔图谱的图像305上的物体检测(或称为物体识别)的结果。物体检测结果可以包括识别出图像中多个物理实体中的一个或者多个的轮廓,或者还可以包括在图像中的物体上叠加边界框。
对比图305和图420可以看出,在较暗的光线情况下,通过本申请实施提供的方案,能够得到较为理想的物体检测结果,该结果优于基于全彩色图像进行物体检测的结果。
图5显示了计算机装置500的一个示例,该装置可以应用本申请以上描述的技术,该装置也可以成为是图像处理装置。例如,计算机装置(或称为硬件平台)500可以实施为包括一个处理器100,或者实施为实现上述实施例的多个模块。硬件平台500可以还包括一个处理器502,该处理器可以执行代码以实现一种方法。硬件平台500可以包括一个存储器504,该存储器用于存储处理器可执行代码和/或存储数据,以实现如图1所示的方法。硬件平台500还可以进一步包括一个通信接口506。例如,通信接口506可以实施一个或多个通信协议(LTE、Wi-Fi,等等)。
本申请实施例还提供了一种计算机可读存储介质,该存储介质中存储有代码,代码被处理器执行后实现如图1所示的方法。
本申请中描述的实质内容以及功能性操作的实施方式,能够通过多种系统、数字电子电路、或者计算机软件、固件或者硬件来实现,这些实施方式包括说明书中公开的结构以及等同结构,或者这些结构的结合。说明书中描述的实质内容的实施方式,能够被实施为一个或者多个计算机程序产品,例如,计算机程序指令的一个或多个模块,该计算机程序指令被编码存储在一个有形且非易失性的计算机可读介质中,该计算机程序指令可被数据处理装置执行,或者用于控制数据处理装置的操作。该计算机可读介质可以是一个机器可读存储装置、机器可读存储基板、存储设备、能够影响机器可读传播信号的组合物、或者这些物质的组合。术语“数据处理单元”或者“数据处理装置”包括用于处理数据的所有装置、设备以及机器,示例性地包括可编程处理器、计算机、或者多处理器、多计算机。除硬件之外,这些装置可以包括为所讨论的计算机程序建立一个可执行环境的代码,例如,构成处理器防火墙、协议栈、数据库管理系统、操作系统的代码,或者这些代码的组合。
计算机程序(也被称为程序、软件、软件应用、脚本或者代码)可以通过任何一种编程语言来编写,包括编译或翻译语音;并且可以被以任何形式来部署,包括一个独立的程序或者一个模块、构件、子用例、或者其它适于计算环境的单元。一个计算机程序不必对应于一个文件系统中的一个文件。一个程序能够被存储在一个文件中的一部分,该文件还存储有其他的程序或者数据(例如,存储在一个标记语言文档中的一个或多个脚本),或者该程序被存储在一个专门针对于所讨论的程序的单独的文件中,或者被存储在一个相互协同的文件中(例如,存储有一个或多个模块、子程序或者代码部分的多个文件)。一个计算机程序可以被部署为被一个或多个计算机执行,这些计算机位于一个地址、或者分布在多个地址,并且通过一个通信网络进行互连。
本说明书描述的处理或者逻辑图可以被一个或者多个可编程处理器执行,以执行一个或多个计算机程序,并根据输入数据进行处理生成输出结果。处理或者逻辑图可以被专用逻辑电路执行,并且多种设备也可以被实现为专用逻辑电路,例如现场可编程门阵列(Field programmable gate array,FPGA)、或者专用集成电路(Application Specificintegrated circuit,ASIC)。
用于执行计算机程序的处理器,示例性地包括通用微处理器和专用微处理器,以及任何种类的数字计算机中的任何一种或者多种处理器。通常,一个处理器会从一个只读存储器和/或一个随机接入存储器中接收指令和数据。一个计算机的基本单元包括一个处理器以及一个或多个存储装置,处理器用于执行指令,存储装置用于存储指令和数据。通常,一个计算机还包括或者操作性地耦合到一个或多个大型存储设备,以接收数据和/或发送数据,该大型存储设备包括磁盘、磁光盘或者光盘。但是,一个计算机不是必须包括这些设备。用于存储指令和数据的计算机可读介质包括所有形式的非易失性存储器、介质以及存储设备,示例性地包括半导体存储设备,例如EPROM、EEPROM以及闪存设备。处理器和存储器可以被专用逻辑电路所替代,或者结合到专用逻辑电路中。
虽然本申请文件包括了多种实施方式,但是这些实施方式不用于解释为本申请的保护范围的限定,只是特征的描述,这些特征可以被实施到特定发明的特定实施例中。本申请中独立的实施例中描述的一些特征也可以被结合实施到一个单独的实施例中。在一个单独的实施例中描述的多个特征也可被分别实施到多个实施例中,或者实施到任何适合的更细一步的结合中。并且,虽然上述在一个特定的组合中描述一些特征,也可以将所要求的一个或者多个组合中去掉一个或多个特征,所要求的组合可以被进一步地组合或者对进一步组合进行变形。
相类似地,虽然在附图中以一定的顺序描述了多种操作,但是不应被理解为必须要以这样的顺序来执行这些操作,以达到理想的结果。并且,实施例中多种系统构件的拆分也不应被理解为在所有的实施例中都需要这样的拆分。
显然,本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样,倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内,则本发明也意图包含这些改动和变型在内。
Claims (12)
1.一种应用于自动驾驶车辆的图像处理方法,其特征在于,包括:
图像处理装置接收来自于一个传感器阵列的图像;其中,该接收到的图像包括二维像素矩阵,所述像素矩阵中的每个像素具有根据一个预设图谱中对应像素的颜色、从该像素的三原色中选择出的对应颜色的值,该预设图谱中的每个像素具有三原色中的一种颜色;
对接收到的图像进行预处理得到预处理图像;
对预处理图像进行感知处理,确定得到物体识别的结果。
2.根据权利要求1所述的方法,其特征在于,在根据所述图谱选择后的图像中,在一行像素中或在一列像素中,每间隔一个像素的颜色为绿色。
3.根据权利要求1所述的方法,其特征在于,在所述图谱中包括的绿色像素值的数量多于红色像素值的数量和蓝色像素值的数量。
4.根据权利要求1所述的方法,其特征在于,所述图谱为拜尔图谱,所述拜尔图谱具有重复排列的2*2像素网格,每个像素网格中的四个像素的颜色分别为红-绿-绿-蓝。
5.根据权利要求1所述的方法,其特征在于,所述接收到的图像是通过与所述图谱对应的一个或多个滤色片、对所述传感器阵列获得的原始图像进行滤色生成的。
6.根据权利要求1所述的方法,其特征在于,所述预处理包括图像裁剪处理、图像缩放处理、和/或图像压缩处理。
7.根据权利要求1所述的方法,其特征在于,在所述预处理包括的多个处理中去除掉去马赛克处理、白平衡处理和/或去噪处理。
8.根据权利要求1所述的方法,其特征在于,物体识别的结果包括识别出图像中多个物理实体中的一个或者多个的轮廓。
9.根据权利要求1所述的方法,其特征在于,物体识别的结果包括在图像中的物理实体上叠加边界框。
10.根据权利要求1所述的方法,其特征在于,所述传感器阵列包括摄像头。
11.一种图像处理装置,其特征在于,包括一个处理器、一个存储器以及一个通信接口,其中该存储器中存储有程序,该程序被处理器执行后实现如权利要求1至10所述方法中的一个或多个。
12.一种计算机可读存储介质,其特征在于,其中存储有代码,该代码被一个处理器执行后实现如权利要求1至10所述方法中的一个或多个。
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US20230351556A1 (en) | 2023-11-02 |
US20210256664A1 (en) | 2021-08-19 |
US11694308B2 (en) | 2023-07-04 |
US20190318456A1 (en) | 2019-10-17 |
US11010874B2 (en) | 2021-05-18 |
CN110378185A (zh) | 2019-10-25 |
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