CN115131314A - Object detection method, device, device and storage medium - Google Patents
Object detection method, device, device and storage medium Download PDFInfo
- Publication number
- CN115131314A CN115131314A CN202210746451.4A CN202210746451A CN115131314A CN 115131314 A CN115131314 A CN 115131314A CN 202210746451 A CN202210746451 A CN 202210746451A CN 115131314 A CN115131314 A CN 115131314A
- Authority
- CN
- China
- Prior art keywords
- image
- detection frame
- detection
- target object
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 297
- 238000000034 method Methods 0.000 claims abstract description 35
- 230000004044 response Effects 0.000 claims abstract description 17
- 238000012549 training Methods 0.000 claims description 59
- 238000004590 computer program Methods 0.000 claims description 10
- 230000001629 suppression Effects 0.000 claims description 8
- 238000002372 labelling Methods 0.000 claims description 3
- 238000005192 partition Methods 0.000 claims 1
- 238000000638 solvent extraction Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 12
- 238000013473 artificial intelligence Methods 0.000 abstract description 4
- 238000013135 deep learning Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 230000008447 perception Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Multimedia (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
Description
技术领域technical field
本公开涉及人工智能技术领域,具体涉及图像处理、计算机视觉、虚拟现实和深度学习等技术领域,可应用于3D视觉、智慧城市、智能交通等场景。The present disclosure relates to the technical field of artificial intelligence, in particular to the technical fields of image processing, computer vision, virtual reality, and deep learning, and can be applied to scenarios such as 3D vision, smart cities, and intelligent transportation.
背景技术Background technique
单目3D(3-Dimension,三维)目标检测(Monocular 3D Object Detection)是应用最广泛的计算机视觉技术之一,该技术可应用于车辆自动驾驶系统、智能机器人、智能交通等领域。目前单目3D目标检测强烈依赖于单个模型来估计整个场景中的目标对象的3D属性,而不同模型对不同距离下的场景感知能力是不同的,不加特殊的划分方式,导致容易朝一个距离范围的场景过拟合,从而影响3D目标检测的性能。Monocular 3D Object Detection (Monocular 3D Object Detection) is one of the most widely used computer vision technologies, which can be applied to vehicle automatic driving systems, intelligent robots, intelligent transportation and other fields. At present, monocular 3D target detection strongly relies on a single model to estimate the 3D properties of the target object in the entire scene, and different models have different scene perception capabilities at different distances. The range of scenes is overfit, which affects the performance of 3D object detection.
发明内容SUMMARY OF THE INVENTION
本公开提供了一种目标检测方法、装置、设备以及存储介质。The present disclosure provides a target detection method, apparatus, device, and storage medium.
根据本公开的第一方面,提供了一种目标检测方法,包括:获取待检测图像;基于深度信息将待检测图像划分为至少两个图像块,其中,至少两个图像块之间存在交叠;针对至少两个图像块中的每个图像块,对图像块进行目标检测,得到图像块中的目标对象对应的检测框;响应于确定同一个目标对象对应至少一个检测框,基于至少一个检测框所处的位置确定该目标对象的目标检测框。According to a first aspect of the present disclosure, there is provided a target detection method, comprising: acquiring an image to be detected; dividing the image to be detected into at least two image blocks based on depth information, wherein there is overlap between the at least two image blocks ; For each image block in the at least two image blocks, perform target detection on the image block to obtain a detection frame corresponding to the target object in the image block; In response to determining that the same target object corresponds to at least one detection frame, based on at least one detection frame The position of the frame determines the target detection frame of the target object.
根据本公开的第二方面,提供了一种目标检测装置,包括:获取模块,被配置成获取待检测图像;划分模块,被配置成基于深度信息将待检测图像划分为至少两个图像块,其中,至少两个图像块之间存在交叠;检测模块,被配置成针对至少两个图像块中的每个图像块,对图像块进行目标检测,得到图像块中的目标对象对应的检测框;确定模块,被配置成响应于确定同一个目标对象对应至少一个检测框,基于至少一个检测框所处的位置确定该目标对象的目标检测框。According to a second aspect of the present disclosure, there is provided a target detection apparatus, comprising: an acquisition module configured to acquire an image to be detected; a division module configured to divide the image to be detected into at least two image blocks based on depth information, Wherein, there is overlap between at least two image blocks; the detection module is configured to perform target detection on the image blocks for each image block in the at least two image blocks, and obtain a detection frame corresponding to the target object in the image blocks a determining module configured to, in response to determining that the same target object corresponds to at least one detection frame, determine a target detection frame of the target object based on the position of the at least one detection frame.
根据本公开的第三方面,提供了一种电子设备,包括:至少一个处理器;以及与至少一个处理器通信连接的存储器;其中,存储器存储有可被至少一个处理器执行的指令,指令被至少一个处理器执行,以使至少一个处理器能够执行如第一方面中任一实现方式描述的方法。According to a third aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor. The at least one processor executes to enable the at least one processor to perform a method as described in any implementation of the first aspect.
根据本公开的第四方面,提供了一种存储有计算机指令的非瞬时计算机可读存储介质,计算机指令用于使计算机执行如第一方面中任一实现方式描述的方法。According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a method as described in any of the implementations of the first aspect.
根据本公开的第五方面,提供了一种计算机程序产品,包括计算机程序,计算机程序在被处理器执行时实现如第一方面中任一实现方式描述的方法。According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method as described in any one of the implementations of the first aspect.
应当理解,本部分所描述的内容并非旨在标识本公开的实施例的关键或重要特征,也不用于限制本公开的范围。本公开的其它特征将通过以下的说明书而变得容易理解。It should be understood that what is described in this section is not intended to identify key or critical features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.
附图说明Description of drawings
附图用于更好地理解本方案,不构成对本公开的限定。其中:The accompanying drawings are used for better understanding of the present solution, and do not constitute a limitation to the present disclosure. in:
图1是本公开可以应用于其中的示例性系统架构图;FIG. 1 is an exemplary system architecture diagram to which the present disclosure may be applied;
图2是根据本公开的目标检测方法的一个实施例的流程图;FIG. 2 is a flowchart of an embodiment of a target detection method according to the present disclosure;
图3是根据本公开的目标检测方法的另一个实施例的流程图;3 is a flowchart of another embodiment of a target detection method according to the present disclosure;
图4是根据本公开的目标检测方法的又一个实施例的流程图;4 is a flow chart of yet another embodiment of a target detection method according to the present disclosure;
图5是根据本公开的目标检测方法的一个应用场景图;5 is an application scenario diagram of the target detection method according to the present disclosure;
图6是根据本公开的目标检测装置的一个实施例的结构示意图;6 is a schematic structural diagram of an embodiment of a target detection apparatus according to the present disclosure;
图7是用来实现本公开实施例的目标检测方法的电子设备的框图。FIG. 7 is a block diagram of an electronic device used to implement the target detection method of an embodiment of the present disclosure.
具体实施方式Detailed ways
以下结合附图对本公开的示范性实施例做出说明,其中包括本公开实施例的各种细节以助于理解,应当将它们认为仅仅是示范性的。因此,本领域普通技术人员应当认识到,可以对这里描述的实施例做出各种改变和修改,而不会背离本公开的范围和精神。同样,为了清楚和简明,以下的描述中省略了对公知功能和结构的描述。Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.
需要说明的是,在不冲突的情况下,本公开中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本公开。It should be noted that the embodiments of the present disclosure and the features of the embodiments may be combined with each other under the condition of no conflict. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.
图1示出了可以应用本公开的目标检测方法或目标检测装置的实施例的示例性系统架构100。FIG. 1 illustrates an
如图1所示,系统架构100可以包括终端设备101、102、103,网络104和服务器105。网络104用以在终端设备101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型,例如有线、无线通信链路或者光纤电缆等等。As shown in FIG. 1 , the
用户可以使用终端设备101、102、103通过网络104与服务器105交互,以接收或发送信息等。终端设备101、102、103上可以安装有各种客户端应用。The user can use the
终端设备101、102、103可以是硬件,也可以是软件。当终端设备101、102、103为硬件时,可以是各种电子设备,包括但不限于智能手机、平板电脑、膝上型便携计算机和台式计算机等等。当终端设备101、102、103为软件时,可以安装在上述电子设备中。其可以实现成多个软件或软件模块,也可以实现成单个软件或软件模块。在此不做具体限定。The
服务器105可以提供各种服务。例如,服务器105可以对从终端设备101、102、103获取的待检测图像进行分析和处理,并生成处理结果(例如目标对象的目标检测框)。The
需要说明的是,服务器105可以是硬件,也可以是软件。当服务器105为硬件时,可以实现成多个服务器组成的分布式服务器集群,也可以实现成单个服务器。当服务器105为软件时,可以实现成多个软件或软件模块(例如用来提供分布式服务),也可以实现成单个软件或软件模块。在此不做具体限定。It should be noted that the
需要说明的是,本公开实施例所提供的目标检测方法一般由服务器105执行,相应地,目标检测装置一般设置于服务器105中。It should be noted that the target detection method provided by the embodiment of the present disclosure is generally executed by the
应该理解,图1中的终端设备、网络和服务器的数目仅仅是示意性的。根据实现需要,可以具有任意数目的终端设备、网络和服务器。It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.
继续参考图2,其示出了根据本公开的目标检测方法的一个实施例的流程200。该目标检测方法包括以下步骤:Continuing to refer to FIG. 2, a
步骤201,获取待检测图像。
在本实施例中,目标检测方法的执行主体(例如图1所示的服务器105)会获取待检测图像。本实施例中的待检测图像可以是由单目摄像机采集的,单目摄像机可以实时地将采集的待检测图像发送给上述执行主体,或者,单目摄像机也可以按照预设的时间间隔将采集的待检测图像发送给上述执行主体。In this embodiment, the execution body of the target detection method (for example, the
步骤202,基于深度信息将待检测图像划分为至少两个图像块。Step 202: Divide the image to be detected into at least two image blocks based on the depth information.
在本实施例中,上述执行主体会基于深度信息将待检测图像划分为至少两个图像块,其中,至少两个图像块之间存在交叠。这里的深度信息指的是深度范围,深度范围也即待检测图像的感知范围,例如0-30米、30-50米等。例如,上述执行主体可以基于距离自适应策略来对待检测图像进行划分,得到不同深度范围的多个图像块,上述执行主体会在图像纵轴上对待检测图像进行划分。一般地,上述执行主体会按照近、中、远三个深度范围来对待检测图像进行划分。In this embodiment, the above-mentioned execution body will divide the image to be detected into at least two image blocks based on the depth information, wherein there is overlap between the at least two image blocks. The depth information here refers to the depth range, which is the perception range of the image to be detected, such as 0-30 meters, 30-50 meters, and so on. For example, the above-mentioned executive body may divide the image to be detected based on a distance adaptive strategy to obtain multiple image blocks of different depth ranges, and the above-mentioned executive body will divide the to-be-detected image on the vertical axis of the image. Generally, the above executive body will divide the image to be detected according to three depth ranges of near, medium and far.
例如,上述执行主体确定待检测图像的整体感知范围为70米,那么其会将待检测图像的整个空间划分为0-30米、30-50米、50-70米这样三个近、中、远距离的图像块,且得到的3个图像块之间会有交叠,从而保证切分图像时图像的边缘信息不会丢失,保证图像信息的完整性。需要说明的是,由于距离近的地方包含的对象会在图像中占更大的像素位置,所以靠下的图像块(也即深度范围最小0-30米的图像块)会更大。For example, if the above-mentioned executive body determines that the overall perception range of the image to be detected is 70 meters, then it will divide the entire space of the image to be detected into three categories: near, medium, and 0-30 meters, 30-50 meters, and 50-70 meters. There is a long-distance image block, and there will be overlap between the three obtained image blocks, so as to ensure that the edge information of the image will not be lost when the image is segmented, and the integrity of the image information will be ensured. It should be noted that since the objects contained in the short distance will occupy a larger pixel position in the image, the lower image blocks (that is, the image blocks with a minimum depth range of 0-30 meters) will be larger.
可选地,在一些场景中,为了保证划分后的图像块的像素比例与未划分图像的像素比例的一致性,上述执行主体会将待检测图像划分为N*N个图像块,也即在纵轴上将待检测图像切分为N块,再在横轴上进行切分,从而得到N*N个图像块,其中,N为正整数。Optionally, in some scenarios, in order to ensure the consistency between the pixel ratio of the divided image block and the pixel ratio of the undivided image, the above-mentioned execution subject will divide the image to be detected into N*N image blocks, that is, in the image block. The image to be detected is divided into N blocks on the vertical axis, and then divided on the horizontal axis to obtain N*N image blocks, where N is a positive integer.
步骤203,针对至少两个图像块中的每个图像块,对图像块进行目标检测,得到图像块中的目标对象对应的检测框。
在本实施例中,针对划分后的至少两个图像块中的每个图像块,上述执行主体会检测该图像块中的目标对象,从而得到目标对象对应的检测框,这里的目标对象可以为任意障碍物,例如车辆、行人等。例如,上述执行主体会先获取每个图像块的深度范围信息,并将该图像块输入至该深度范围对应的检测模型中,以使检测模型对该图像块中的目标对象进行检测,从而得到目标对象对应的检测框。在这里,为每个深度范围预先训练了对应的检测模型,也即对于0-30米这个深度范围,会收集该深度范围的训练数据,并使用该训练数据进行训练,得到对应的检测模型。由于待检测图像场景覆盖的距离范围较大,直接用单模型预测场景中所有对象的3D属性较为困难,回归值往往不是很准确。所以,本实施例中将回归任务转换为分类任务,先对待检测图像进行分类和划分,再用不同的检测模型分别进行3D检测,从而提升了检测结果的准确性。In this embodiment, for each image block in the divided at least two image blocks, the execution subject will detect the target object in the image block, so as to obtain a detection frame corresponding to the target object, where the target object may be Arbitrary obstacles, such as vehicles, pedestrians, etc. For example, the above-mentioned executive body will first obtain the depth range information of each image block, and input the image block into the detection model corresponding to the depth range, so that the detection model can detect the target object in the image block, thereby obtaining The detection frame corresponding to the target object. Here, the corresponding detection model is pre-trained for each depth range, that is, for the depth range of 0-30 meters, the training data of the depth range will be collected, and the training data will be used for training to obtain the corresponding detection model. Due to the large distance range covered by the image scene to be detected, it is difficult to directly predict the 3D attributes of all objects in the scene with a single model, and the regression values are often not very accurate. Therefore, in this embodiment, the regression task is converted into a classification task, and the images to be detected are first classified and divided, and then different detection models are used to perform 3D detection respectively, thereby improving the accuracy of the detection results.
步骤204,响应于确定同一个目标对象对应至少一个检测框,基于至少一个检测框所处的位置确定该目标对象的目标检测框。
在本实施例中,由于不同的图像块之间有交叠,而每个图像块都使用了对应的检测模型进行检测,所以不同图像块中的交叠部分会有多个不同的检测框,也即同一个目标对象会对应多个检测框。此外,即使对于同一个图像块中的同一个目标对象,其也可能对应于多个检测框。基于此,上述执行主体会基于多个检测框所处的位置信息来为目标对象从多个检测框中确定目标检测框。In this embodiment, since there is overlap between different image blocks, and each image block uses a corresponding detection model for detection, there will be multiple different detection frames in the overlapping portion of different image blocks, That is, the same target object will correspond to multiple detection frames. Furthermore, even for the same target object in the same image patch, it may correspond to multiple detection boxes. Based on this, the above-mentioned execution subject determines the target detection frame from the plurality of detection frames for the target object based on the position information of the plurality of detection frames.
例如,若同一个目标对象对应的多个检测框处于同一个图像块中,那么可以基于检测框的置信度值来确定目标检测框。若同一个目标对象对应的多个检测框处于不同图像块中,那么上述执行主体会基于非极大值抑制(Non-Maximum Suppression,NMS)方法,来从多个检测框中确定该目标对象的目标检测框。NMS算法广泛应用于目标检测场景中,其目的是为了消除多余的候选框,找到最佳的检测框。For example, if multiple detection frames corresponding to the same target object are in the same image block, the target detection frame may be determined based on the confidence value of the detection frame. If multiple detection frames corresponding to the same target object are in different image blocks, the above-mentioned execution subject will determine the target object from multiple detection frames based on the Non-Maximum Suppression (NMS) method. Object detection box. The NMS algorithm is widely used in target detection scenarios, and its purpose is to eliminate redundant candidate frames and find the best detection frame.
本公开实施例提供的目标检测方法,首先获取待检测图像;然后基于深度信息将待检测图像划分为至少两个图像块;之后针对至少两个图像块中的每个图像块,对该图像块进行目标检测,得到该图像块中的目标对象对应的检测框;最后响应于确定同一个目标对象对应至少一个检测框,基于至少一个检测框所处的位置确定该目标对象的目标检测框。本实施例中的目标检测方法,该方法先对待检测图像按照深度范围进行划分,然后再进行不同距离范围下的检测,从而提升了3D目标检测的精度,也提升了3D目标检测结果的准确性。In the target detection method provided by the embodiment of the present disclosure, the image to be detected is first acquired; then the image to be detected is divided into at least two image blocks based on the depth information; and then for each image block in the at least two image blocks, the image block is Perform target detection to obtain a detection frame corresponding to the target object in the image block; finally, in response to determining that the same target object corresponds to at least one detection frame, the target detection frame of the target object is determined based on the position of the at least one detection frame. In the target detection method in this embodiment, the method first divides the image to be detected according to the depth range, and then performs detection in different distance ranges, thereby improving the accuracy of 3D target detection and the accuracy of 3D target detection results. .
本公开的技术方案中,所涉及的用户个人信息的收集、存储、使用、加工、传输、提供和公开等处理,均符合相关法律法规的规定,且不违背公序良俗。In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved are all in compliance with relevant laws and regulations, and do not violate public order and good customs.
继续参考图3,图3示出了根据本公开的目标检测方法的另一个实施例的流程300。该目标检测方法包括以下步骤:Continuing to refer to FIG. 3 , FIG. 3 illustrates a
步骤301,获取待检测图像。
步骤302,基于深度信息将待检测图像划分为至少两个图像块。Step 302: Divide the image to be detected into at least two image blocks based on the depth information.
步骤303,针对至少两个图像块中的每个图像块,对图像块进行目标检测,得到图像块中的目标对象对应的检测框。
步骤301-303与前述实施例的步骤201-203基本一致,具体实现方式可以参考前述对步骤201-203的描述,此处不再赘述。
步骤304,响应于确定同一个目标对象对应的至少一个检测框处于同一个图像块中,确定每个检测框的置信度,将置信度值最高的检测框作为该目标对象的目标检测框。
在本实施例中,若确定同一个目标对象对应的至少一个检测框处于同一个图像块中,目标检测方法的执行主体(例如图1所示的服务器105)会基于每个检测框的置信度值,来确定目标检测框。也即上述执行主体会获取每个检测框的置信度值,然后将置信度值最高的检测框作为该目标对象的目标检测框。当使用检测模型对图像块进行检测时,检测模型还会输出每个检测框的置信度值,上述执行主体可以直接获取每个检测框的置信度值,从而基于置信度值确定目标检测框。从而保证目标检测框的准确度。In this embodiment, if it is determined that at least one detection frame corresponding to the same target object is in the same image block, the execution body of the target detection method (for example, the
步骤305,响应于确定同一个目标对象对应的至少一个检测框处于存在交叠的不同图像块中,基于非极大值抑制方法,从至少一个检测框中确定该目标对象的目标检测框。
在本实施例中,若确定同一个目标对象对应的至少一个检测框处于存在交叠的不同图像块中,上述执行主体会基于非极大值抑制方法,从多个检测框中确定该目标对象的目标检测框。NMS算法广泛应用于目标检测场景中,其目的是为了消除多余的候选框,找到最佳的检测框。从而保证目标检测框的准确度。In this embodiment, if it is determined that at least one detection frame corresponding to the same target object is in different overlapping image blocks, the execution subject will determine the target object from multiple detection frames based on the non-maximum value suppression method target detection frame. The NMS algorithm is widely used in target detection scenarios, and its purpose is to eliminate redundant candidate frames and find the best detection frame. Thereby ensuring the accuracy of the target detection frame.
在本实施例的一些可选的实现方式中,步骤305包括:针对至少一个检测框中的每个检测框,基于检测框所在图像块的深度信息以及检测框在图像块中的位置信息,确定检测框的距离权重;基于检测框的置信度以及距离权重,确定检测框的分数;将至少一个检测框中分数最高的检测框,作为该目标对象的目标检测框。In some optional implementations of this embodiment,
在本实现方式中,由于同一个目标对象对应的多个检测框处于存在交叠的多个图像块中,上述执行主体会先确定每个检测框所在图像块的深度信息,并确定该目标对象的检测框在图像块中的位置信息,基于深度信息和位置信息来为该检测框设置距离权重。一般地,由于近处的感知能力相对更准,所以会为近处距离的且占比更大的检测框设置一个更高的距离权重。然后再用该距离权重乘以该检测框的置信度值,从而得到该检测框的最终分数。最后,将至少一个检测框中分数最高的检测框作为该目标对象的目标检测框。从而可以保证确定的目标检测框具有更强的检测能力。In this implementation manner, since multiple detection frames corresponding to the same target object are in multiple overlapping image blocks, the above-mentioned execution subject will first determine the depth information of the image block where each detection frame is located, and then determine the target object. The position information of the detection frame in the image block, and the distance weight is set for the detection frame based on the depth information and position information. Generally, since the perception ability of the near distance is relatively more accurate, a higher distance weight will be set for the detection frame that is close to the distance and accounts for a larger proportion. Then multiply the confidence value of the detection box by the distance weight to get the final score of the detection box. Finally, the detection frame with the highest score in at least one detection frame is used as the target detection frame of the target object. Therefore, it can be ensured that the determined target detection frame has a stronger detection capability.
作为示例,假设将图像Y按照深度信息进行划分后得到近距离图像块A与中距离图像块B,近距离图像块A与中距离图像块B存在交叠,目标对象X在近距离图像块A中的检测框为A1,A1在A中的占比为60%,且A1的置信度为0.9。目标对象X在中距离图像块B中的检测框为B1,B1在B中的占比为30%,且B1的置信度为0.6。由于A1所在图像块距离更近,且A1占比更大,那么上述执行主体会将A1的距离权重设置为1.2,将B1的距离权重设置为1.0。因此,可以计算得到A1的分数为1.2*0.9=1.08,B1的分数为1.0*0.6=0.6。因此,上述执行主体会将A1作为目标对象X的目标检测框。As an example, suppose that the image Y is divided according to the depth information to obtain a close-range image block A and a medium-distance image block B, the close-range image block A and the intermediate-distance image block B overlap, and the target object X is in the close-range image block A. The detection frame in A1 is A1, the proportion of A1 in A is 60%, and the confidence level of A1 is 0.9. The detection frame of the target object X in the mid-range image block B is B1, the proportion of B1 in B is 30%, and the confidence level of B1 is 0.6. Since the image block where A1 is located is closer and A1 occupies a larger proportion, the above execution subject will set the distance weight of A1 to 1.2 and the distance weight of B1 to 1.0. Therefore, it can be calculated that the score of A1 is 1.2*0.9=1.08, and the score of B1 is 1.0*0.6=0.6. Therefore, the above executive body will use A1 as the target detection frame of the target object X.
从图3中可以看出,与图2对应的实施例相比,本实施例中的目标检测方法,该方法突出了确定目标检测框的过程,从而保证了确定的目标检测框具有更好的检测能力,提升了目标检测结果的准确性。As can be seen from FIG. 3 , compared with the embodiment corresponding to FIG. 2 , the target detection method in this embodiment highlights the process of determining the target detection frame, thereby ensuring that the determined target detection frame has better The detection ability improves the accuracy of target detection results.
继续参考图4,图4示出了根据本公开的目标检测方法的又一个实施例的流程400。该目标检测方法包括以下步骤:Continuing to refer to FIG. 4 , FIG. 4 shows a
步骤401,获取待检测图像。
步骤402,将待检测图像输入至划分模型中,输出得到划分后的至少两个图像块。Step 402: Input the image to be detected into the division model, and output at least two divided image blocks.
在本实施例中,目标检测方法的执行主体(例如图1所示的服务器105)会将待检测图像输入至划分模型中,输出得到划分后的至少两个图像块,其中,划分模型用于将待检测图像按照不同的深度信息划分为至少两个图像块。也即在本实施例中,会预先训练划分模型,以使划分模型可以将待检测图像按照深度信息划分为多个图像块。由于划分模型具有按照深度信息区间对图像进行划分的能力,使用划分模型进行划分,可以提升划分效率,还可以保证划分结果的合理性。In this embodiment, the execution body of the target detection method (for example, the
在本实施例的一些可选的实现方式中,划分模型基于以下步骤训练得到:获取训练样本集,其中,训练样本集中的训练样本包括样本图像以及样本图像对应的子图像,子图像为对样本图像按照深度信息进行划分得到;将样本图像作为输入,将样本图像对应的子图像作为输出,训练初始划分模型,得到训练完成的划分模型。In some optional implementations of this embodiment, the division model is obtained by training based on the following steps: acquiring a training sample set, wherein the training samples in the training sample set include sample images and sub-images corresponding to the sample images, and the sub-images are pairs of samples The image is obtained by dividing it according to the depth information; taking the sample image as input, and using the sub-image corresponding to the sample image as output, train the initial division model, and obtain the trained division model.
在本实现方式中,上述执行主体会先获取样本图像,以及每个样本图像对应的多个子图像,将样本图像以及样本图像对应的子图像作为训练样本,从而得到训练样本集,这里可以采用人工划分的方式来将样本图像按照不同深度信息划分为多个子图像。之后,上述执行主体会将样本图像作为输入,将样本图像对应的子图像作为输出,训练初始划分模型,从而得到训练完成的划分模型。通过上述方式训练得到的划分模型可以按照深度范围快速、准确地对待检测图像进行划分。In this implementation manner, the above-mentioned execution body will first obtain a sample image and a plurality of sub-images corresponding to each sample image, and use the sample image and the sub-images corresponding to the sample image as training samples to obtain a training sample set. The sample image is divided into multiple sub-images according to different depth information. After that, the above-mentioned execution subject will use the sample image as input, and use the sub-image corresponding to the sample image as output to train the initial division model, thereby obtaining the trained division model. The division model trained in the above manner can quickly and accurately divide the image to be detected according to the depth range.
步骤403,针对至少两个图像块中的每个图像块,确定图像块的深度信息。
在本实施例中,针对至少两个图像块中的每个图像块,上述执行主体会先确定该图像块的深度信息。由于在对待检测图像进行划分时,是按照深度信息进行划分的,这里上述执行主体会再获取每个图像块划分时的深度信息。In this embodiment, for each image block in the at least two image blocks, the above-mentioned execution body will first determine the depth information of the image block. Since when the image to be detected is divided, it is divided according to the depth information, and here the above-mentioned execution subject will obtain the depth information when each image block is divided.
步骤404,将图像块输入至深度信息对应的检测模型中,输出得到图像块中的目标对象对应的检测框。Step 404: Input the image block into the detection model corresponding to the depth information, and output the detection frame corresponding to the target object in the image block.
在本实施例中,在确定每个图像块的深度信息后,上述执行主体会确定该深度信息对应的检测模型,并将该图像块输入至该检测模型中,从而得到图像块中目标对象对应的检测框。在这里,会预先训练好多个深度信息对应的检测模型,从而可以准确地对每个图像块进行检测,提升检测结果的准确性。In this embodiment, after determining the depth information of each image block, the above-mentioned execution body will determine the detection model corresponding to the depth information, and input the image block into the detection model, so as to obtain the corresponding target object in the image block. the detection frame. Here, multiple detection models corresponding to depth information will be pre-trained, so that each image block can be accurately detected and the accuracy of the detection results can be improved.
在本实施例的一些可选的实现方式中,检测模型基于以下步骤训练得到:获取训练数据集,其中,训练数据集中的训练数据包括原始图像以及原始图像对应的检测框,训练数据集中的训练数据具有相同的深度信息,检测框为对原始图像中的目标对象进行标注得到;将原始图像作为输入,将原始图像对应的检测框作为输出,训练初始检测模型,得到训练完成的检测模型。In some optional implementations of this embodiment, the detection model is obtained by training based on the following steps: acquiring a training data set, wherein the training data in the training data set includes the original image and the detection frame corresponding to the original image, and the training data in the training data set includes the original image and the detection frame corresponding to the original image. The data has the same depth information, and the detection frame is obtained by labeling the target object in the original image; the original image is used as the input, the detection frame corresponding to the original image is used as the output, the initial detection model is trained, and the trained detection model is obtained.
在本实现方式中,上述执行主体会先获取原始图像,以及每个原始图像对应的检测框,将原始图像以及原始图像对应的检测框作为训练数据,从而得到训练数据集,这里可以采用人工标注的方式来对原始图像中的目标对象进行标注,从而得到对应的检测框。之后,上述执行主体会将原始图像作为输入,将原始图像对应的检测框作为输出,训练初始检测模型,从而得到训练完成的检测模型。通过上述方式训练得到的检测模型可以快速、准确地对图像块中的目标对象进行检测。In this implementation, the above-mentioned execution subject will first obtain the original image and the detection frame corresponding to each original image, and use the original image and the detection frame corresponding to the original image as training data to obtain a training data set, where manual annotation can be used. method to annotate the target object in the original image, so as to obtain the corresponding detection frame. After that, the above-mentioned execution subject will use the original image as input, and use the detection frame corresponding to the original image as output to train the initial detection model, thereby obtaining the trained detection model. The detection model trained in the above manner can quickly and accurately detect the target object in the image block.
步骤405,响应于确定同一个目标对象对应的至少一个检测框处于同一个图像块中,分别计算每个检测框的置信度,将置信度值最高的检测框作为该目标对象的目标检测框。
步骤406,响应于确定同一个目标对象对应的至少一个检测框处于存在交叠的不同图像块中,基于非极大值抑制方法,从至少一个检测框中确定该目标对象的目标检测框。
步骤405-406与前述实施例的步骤304-305基本一致,具体实现方式可以参考前述对步骤304-305的描述,此处不再赘述。Steps 405-406 are basically the same as steps 304-305 in the foregoing embodiment. For a specific implementation manner, reference may be made to the foregoing description of steps 304-305, and details are not repeated here.
从图4中可以看出,与图3对应的实施例相比,本实施例中的目标检测方法,该方法突出了对待检测图像进行划分的步骤以及确定图像块中的目标对象对应的检测框的步骤,从而进一步提升了3D目标检测的效率和准确度。As can be seen from FIG. 4 , compared with the embodiment corresponding to FIG. 3 , the target detection method in this embodiment highlights the steps of dividing the image to be detected and determining the detection frame corresponding to the target object in the image block. , which further improves the efficiency and accuracy of 3D target detection.
进一步参考图5,图5示出了根据本公开的目标检测方法的一个应用场景图。在该应用场景中,执行主体会将待检测图像输入至距离切分网络中,以使距离切分网络按照深度信息对待检测图像进行划分,得到划分后的3*3个图像块,这3*3个图像块包括近距离三个图像块、中距离三个图像块以及远距离三个图像块,且这3*3个图像块之间有交叠,从而保证划分图像时图像的边缘信息不会丢失,且可以看出,由于距离近的地方包含的障碍物会在图像中占更大的像素位置,所以靠下的图像块会更大。Further referring to FIG. 5 , FIG. 5 shows an application scenario diagram of the target detection method according to the present disclosure. In this application scenario, the execution subject will input the image to be detected into the distance segmentation network, so that the distance segmentation network divides the image to be detected according to the depth information, and obtains divided 3*3 image blocks, these 3* The 3 image blocks include three image blocks at close range, three image blocks at medium distance, and three image blocks at long distance, and these 3*3 image blocks overlap each other, so as to ensure that the edge information of the image is not changed when dividing the image. will be lost, and it can be seen that the lower image blocks will be larger because the obstacles contained in the close distance will occupy a larger pixel position in the image.
之后,上述执行主体会将每个图像块输入至对应的感知网络中,从而得到每个图像块中的目标对象对应的检测框。例如将近距离三个图像块输入至近距离感知网络中,将中距离三个图像块输入至中距离感知网络中,将远距离三个图像块输入至远距离感知网络中。After that, the above-mentioned executive body will input each image block into the corresponding perceptual network, so as to obtain the detection frame corresponding to the target object in each image block. For example, inputting three image blocks at a short distance into the short-range sensing network, inputting three image blocks at a medium distance into the middle-distance sensing network, and inputting three image blocks at a long distance into the long-distance sensing network.
最后,基于NMS策略来进行距离感知加权,即由于近处的感知能力相对更准,所以会为近处距离的检测框设置一个更高的权重值,然后再将所有距离网络得到的3D检测框合在一起进行NMS,也即基于每个3D检测框的权重以及置信度值,确定最终的目标检测框。Finally, the distance perception weighting is performed based on the NMS strategy, that is, since the near perception ability is relatively more accurate, a higher weight value will be set for the detection frame of the near distance, and then all the 3D detection frames obtained by the distance network will be used. NMS is performed together, that is, the final target detection frame is determined based on the weight and confidence value of each 3D detection frame.
进一步参考图6,作为对上述各图所示方法的实现,本公开提供了一种目标检测装置的一个实施例,该装置实施例与图2所示的方法实施例相对应,该装置具体可以应用于各种电子设备中。Further referring to FIG. 6 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a target detection apparatus, the apparatus embodiment corresponds to the method embodiment shown in FIG. 2 , and the apparatus may specifically Used in various electronic devices.
如图6所示,本实施例的目标检测装置600包括:获取模块601、划分模块602、检测模块603和确定模块604。其中,获取模块601,被配置成获取待检测图像;划分模块602,被配置成基于深度信息将待检测图像划分为至少两个图像块,其中,至少两个图像块之间存在交叠;检测模块603,被配置成针对至少两个图像块中的每个图像块,对图像块进行目标检测,得到图像块中的目标对象对应的检测框;确定模块604,被配置成响应于确定同一个目标对象对应至少一个检测框,基于至少一个检测框所处的位置确定该目标对象的目标检测框。As shown in FIG. 6 , the
在本实施例中,目标检测装置600中:获取模块601、划分模块602、检测模块603和确定模块604的具体处理及其所带来的技术效果可分别参考图2对应实施例中的步骤201-204的相关说明,在此不再赘述。In this embodiment, in the target detection device 600: the specific processing of the
在本实施例的一些可选的实现方式中,确定模块包括:第一确定子模块,被配置成响应于确定同一个目标对象对应的至少一个检测框处于同一个图像块中,确定每个检测框的置信度,将置信度值最高的检测框作为该目标对象的目标检测框;第二确定子模块,被配置成响应于确定同一个目标对象对应的至少一个检测框处于存在交叠的不同图像块中,基于非极大值抑制方法,从至少一个检测框中确定该目标对象的目标检测框。In some optional implementations of this embodiment, the determining module includes: a first determining sub-module, configured to, in response to determining that at least one detection frame corresponding to the same target object is in the same image block, determine each detection frame The confidence level of the frame, the detection frame with the highest confidence value is used as the target detection frame of the target object; the second determination sub-module is configured to respond to determining that at least one detection frame corresponding to the same target object is in a different overlapping area In the image block, the target detection frame of the target object is determined from at least one detection frame based on the non-maximum value suppression method.
在本实施例的一些可选的实现方式中,第二确定子模块被进一步配置成:针对至少一个检测框中的每个检测框,基于检测框所在图像块的深度信息以及检测框在图像块中的位置信息,确定检测框的距离权重;基于检测框的置信度以及距离权重,确定检测框的分数;将至少一个检测框中分数最高的检测框,作为该目标对象的目标检测框。In some optional implementations of this embodiment, the second determination submodule is further configured to: for each detection frame in the at least one detection frame, based on the depth information of the image block where the detection frame is located and the detection frame in the image block Determine the distance weight of the detection frame based on the location information in the detection frame; determine the score of the detection frame based on the confidence of the detection frame and the distance weight; take the detection frame with the highest score in at least one detection frame as the target detection frame of the target object.
在本实施例的一些可选的实现方式中,划分模块包括:划分子模块,被配置成将待检测图像输入至划分模型中,输出得到划分后的至少两个图像块,其中,划分模型用于将待检测图像按照不同的深度信息划分为至少两个图像块。In some optional implementations of this embodiment, the division module includes: a division sub-module, configured to input the image to be detected into the division model, and output at least two divided image blocks, wherein the division model uses The image to be detected is divided into at least two image blocks according to different depth information.
在本实施例的一些可选的实现方式中,上述目标检测装置600还包括用于训练划分模型的第一训练模块,第一训练模块被配置成:获取训练样本集,其中,训练样本集中的训练样本包括样本图像以及样本图像对应的子图像,子图像为对样本图像按照深度信息进行划分得到;将样本图像作为输入,将样本图像对应的子图像作为输出,训练初始划分模型,得到训练完成的划分模型。In some optional implementations of this embodiment, the above-mentioned
在本实施例的一些可选的实现方式中,检测模块包括:第三确定子模块,被配置成确定图像块的深度信息;检测子模块,被配置成将图像块输入至深度信息对应的检测模型中,输出得到图像块中的目标对象对应的检测框。In some optional implementations of this embodiment, the detection module includes: a third determination submodule, configured to determine depth information of the image block; and a detection submodule, configured to input the image block into the detection corresponding to the depth information In the model, the output is the detection frame corresponding to the target object in the image block.
在本实施例的一些可选的实现方式中,上述目标检测装置600用于训练检测模型的第二训练模块,第二训练模块被配置成:获取训练数据集,其中,训练数据集中的训练数据包括原始图像以及原始图像对应的检测框,训练数据集中的训练数据具有相同的深度信息,检测框为对原始图像中的目标对象进行标注得到;将原始图像作为输入,将原始图像对应的检测框作为输出,训练初始检测模型,得到训练完成的检测模型。In some optional implementations of this embodiment, the above-mentioned
根据本公开的实施例,本公开还提供了一种电子设备、一种可读存储介质和一种计算机程序产品。According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.
图7示出了可以用来实施本公开的实施例的示例电子设备700的示意性框图。电子设备旨在表示各种形式的数字计算机,诸如,膝上型计算机、台式计算机、工作台、个人数字助理、服务器、刀片式服务器、大型计算机、和其它适合的计算机。电子设备还可以表示各种形式的移动装置,诸如,个人数字处理、蜂窝电话、智能电话、可穿戴设备和其它类似的计算装置。本文所示的部件、它们的连接和关系、以及它们的功能仅仅作为示例,并且不意在限制本文中描述的和/或者要求的本公开的实现。FIG. 7 shows a schematic block diagram of an example
如图7所示,设备700包括计算单元701,其可以根据存储在只读存储器(ROM)702中的计算机程序或者从存储单元708加载到随机访问存储器(RAM)703中的计算机程序,来执行各种适当的动作和处理。在RAM 703中,还可存储设备700操作所需的各种程序和数据。计算单元701、ROM 702以及RAM 703通过总线704彼此相连。输入/输出(I/O)接口705也连接至总线704。As shown in FIG. 7 , the
设备700中的多个部件连接至I/O接口705,包括:输入单元706,例如键盘、鼠标等;输出单元707,例如各种类型的显示器、扬声器等;存储单元708,例如磁盘、光盘等;以及通信单元709,例如网卡、调制解调器、无线通信收发机等。通信单元709允许设备700通过诸如因特网的计算机网络和/或各种电信网络与其他设备交换信息/数据。Various components in the
计算单元701可以是各种具有处理和计算能力的通用和/或专用处理组件。计算单元701的一些示例包括但不限于中央处理单元(CPU)、图形处理单元(GPU)、各种专用的人工智能(AI)计算芯片、各种运行机器学习模型算法的计算单元、数字信号处理器(DSP)、以及任何适当的处理器、控制器、微控制器等。计算单元701执行上文所描述的各个方法和处理,例如目标检测方法。例如,在一些实施例中,目标检测方法可被实现为计算机软件程序,其被有形地包含于机器可读介质,例如存储单元708。在一些实施例中,计算机程序的部分或者全部可以经由ROM 702和/或通信单元709而被载入和/或安装到设备700上。当计算机程序加载到RAM 703并由计算单元701执行时,可以执行上文描述的目标检测方法的一个或多个步骤。备选地,在其他实施例中,计算单元701可以通过其他任何适当的方式(例如,借助于固件)而被配置为执行目标检测方法。
本文中以上描述的系统和技术的各种实施方式可以在数字电子电路系统、集成电路系统、场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、芯片上系统的系统(SOC)、复杂可编程逻辑设备(CPLD)、计算机硬件、固件、软件、和/或它们的组合中实现。这些各种实施方式可以包括:实施在一个或者多个计算机程序中,该一个或者多个计算机程序可在包括至少一个可编程处理器的可编程系统上执行和/或解释,该可编程处理器可以是专用或者通用可编程处理器,可以从存储系统、至少一个输入装置、和至少一个输出装置接收数据和指令,并且将数据和指令传输至该存储系统、该至少一个输入装置、和该至少一个输出装置。Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), complex programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.
用于实施本公开的方法的程序代码可以采用一个或多个编程语言的任何组合来编写。这些程序代码可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器或控制器,使得程序代码当由处理器或控制器执行时使流程图和/或框图中所规定的功能/操作被实施。程序代码可以完全在机器上执行、部分地在机器上执行,作为独立软件包部分地在机器上执行且部分地在远程机器上执行或完全在远程机器或服务器上执行。Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.
在本公开的上下文中,机器可读介质可以是有形的介质,其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。机器可读介质可以是机器可读信号介质或机器可读储存介质。机器可读介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备,或者上述内容的任何合适组合。机器可读存储介质的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.
为了提供与用户的交互,可以在计算机上实施此处描述的系统和技术,该计算机具有:用于向用户显示信息的显示装置(例如,CRT(阴极射线管)或者LCD(液晶显示器)监视器);以及键盘和指向装置(例如,鼠标或者轨迹球),用户可以通过该键盘和该指向装置来将输入提供给计算机。其它种类的装置还可以用于提供与用户的交互;例如,提供给用户的反馈可以是任何形式的传感反馈(例如,视觉反馈、听觉反馈、或者触觉反馈);并且可以用任何形式(包括声输入、语音输入或者、触觉输入)来接收来自用户的输入。To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.
可以将此处描述的系统和技术实施在包括后台部件的计算系统(例如,作为数据服务器)、或者包括中间件部件的计算系统(例如,应用服务器)、或者包括前端部件的计算系统(例如,具有图形用户界面或者网络浏览器的用户计算机,用户可以通过该图形用户界面或者该网络浏览器来与此处描述的系统和技术的实施方式交互)、或者包括这种后台部件、中间件部件、或者前端部件的任何组合的计算系统中。可以通过任何形式或者介质的数字数据通信(例如,通信网络)来将系统的部件相互连接。通信网络的示例包括:局域网(LAN)、广域网(WAN)和互联网。The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
云计算(cloud computer),指的是通过网络接入弹性可扩展的共享物理或虚拟资源池,资源可以包括服务器、操作系统、网络、软件、应用或存储设备等,并可以以按需、自服务的方式对资源进行部署和管理的技术体系。通过云计算技术,可以为人工智能、区块链等技术应用、模型训练提供高效强大的数据处理能力。Cloud computing refers to accessing an elastically scalable shared physical or virtual resource pool through a network. Resources can include servers, operating systems, networks, software, applications or storage devices, etc. A technical system for deploying and managing resources in the form of services. Through cloud computing technology, it can provide efficient and powerful data processing capabilities for artificial intelligence, blockchain and other technical applications and model training.
计算机系统可以包括客户端和服务器。客户端和服务器一般远离彼此并且通常通过通信网络进行交互。通过在相应的计算机上运行并且彼此具有客户端-服务器关系的计算机程序来产生客户端和服务器的关系。服务器可以是云服务器,也可以为分布式系统的服务器,或者是结合了区块链的服务器。A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a distributed system server, or a server combined with blockchain.
应该理解,可以使用上面所示的各种形式的流程,重新排序、增加或删除步骤。例如,本发公开中记载的各步骤可以并行地执行也可以顺序地执行也可以不同的次序执行,只要能够实现本公开公开的技术方案所期望的结果,本文在此不进行限制。It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in different orders. As long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, there is no limitation herein.
上述具体实施方式,并不构成对本公开保护范围的限制。本领域技术人员应该明白的是,根据设计要求和其他因素,可以进行各种修改、组合、子组合和替代。任何在本公开的精神和原则之内所作的修改、等同替换和改进等,均应包含在本公开保护范围之内。The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.
Claims (17)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210746451.4A CN115131314A (en) | 2022-06-28 | 2022-06-28 | Object detection method, device, device and storage medium |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210746451.4A CN115131314A (en) | 2022-06-28 | 2022-06-28 | Object detection method, device, device and storage medium |
Publications (1)
Publication Number | Publication Date |
---|---|
CN115131314A true CN115131314A (en) | 2022-09-30 |
Family
ID=83379441
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210746451.4A Pending CN115131314A (en) | 2022-06-28 | 2022-06-28 | Object detection method, device, device and storage medium |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115131314A (en) |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108510472A (en) * | 2018-03-08 | 2018-09-07 | 北京百度网讯科技有限公司 | Method and apparatus for handling image |
CN108564030A (en) * | 2018-04-12 | 2018-09-21 | 广州飒特红外股份有限公司 | Classifier training method and apparatus towards vehicle-mounted thermal imaging pedestrian detection |
CN110084173A (en) * | 2019-04-23 | 2019-08-02 | 精伦电子股份有限公司 | Number of people detection method and device |
JP2021013056A (en) * | 2019-07-03 | 2021-02-04 | キヤノン株式会社 | Image processing device, imaging apparatus, image processing method, and program |
CN114004788A (en) * | 2021-09-23 | 2022-02-01 | 中大(海南)智能科技有限公司 | Defect detection method, device, equipment and storage medium |
-
2022
- 2022-06-28 CN CN202210746451.4A patent/CN115131314A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108510472A (en) * | 2018-03-08 | 2018-09-07 | 北京百度网讯科技有限公司 | Method and apparatus for handling image |
CN108564030A (en) * | 2018-04-12 | 2018-09-21 | 广州飒特红外股份有限公司 | Classifier training method and apparatus towards vehicle-mounted thermal imaging pedestrian detection |
CN110084173A (en) * | 2019-04-23 | 2019-08-02 | 精伦电子股份有限公司 | Number of people detection method and device |
JP2021013056A (en) * | 2019-07-03 | 2021-02-04 | キヤノン株式会社 | Image processing device, imaging apparatus, image processing method, and program |
CN114004788A (en) * | 2021-09-23 | 2022-02-01 | 中大(海南)智能科技有限公司 | Defect detection method, device, equipment and storage medium |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP4033453A1 (en) | Training method and apparatus for target detection model, device and storage medium | |
KR102721493B1 (en) | Lane marking detecting method, apparatus, electronic device, storage medium, and vehicle | |
US20210272306A1 (en) | Method for training image depth estimation model and method for processing image depth information | |
CN113920307A (en) | Model training method, device, equipment, storage medium and image detection method | |
CN112966742A (en) | Model training method, target detection method and device and electronic equipment | |
CN113674421B (en) | 3D target detection method, model training method, related device and electronic equipment | |
CN114549612A (en) | Model training and image processing method, device, equipment and storage medium | |
US20230245429A1 (en) | Method and apparatus for training lane line detection model, electronic device and storage medium | |
CN113378712B (en) | Training method of object detection model, image detection method and device thereof | |
CN113762397B (en) | Method, equipment, medium and product for training detection model and updating high-precision map | |
CN113901998A (en) | Model training method, device, equipment, storage medium and detection method | |
WO2023155387A1 (en) | Multi-sensor target detection method and apparatus, electronic device and storage medium | |
CN113177497B (en) | Training method of visual model, vehicle identification method and device | |
CN114882316A (en) | Target detection model training method, target detection method and device | |
US20230162383A1 (en) | Method of processing image, device, and storage medium | |
CN115578516A (en) | A three-dimensional imaging method, device, equipment and storage medium | |
CN113409340A (en) | Semantic segmentation model training method, semantic segmentation device and electronic equipment | |
CN113205131A (en) | Image data processing method and device, road side equipment and cloud control platform | |
KR20230133808A (en) | Method and apparatus for training roi detection model, method and apparatus for detecting roi, device, and medium | |
CN115147831A (en) | Training method and device of three-dimensional target detection model | |
CN114037630A (en) | Model training and image defogging method, device, equipment and storage medium | |
CN114092874B (en) | Training method of target detection model, target detection method and related equipment thereof | |
CN115131314A (en) | Object detection method, device, device and storage medium | |
CN116597213A (en) | Target detection method, training device, electronic equipment and storage medium | |
CN115375740A (en) | Pose determination method, three-dimensional model generation method, device, equipment and medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |