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Image processing method and device, electronic equipment and storage medium

An image processing and processing module technology, applied in the field of image processing, can solve problems such as large amount of calculation, difficulty in model learning, high error rate of feature alignment, etc., to achieve the effect of improving accuracy, reducing calculation amount, and reducing learning difficulty

Pending Publication Date: 2022-06-17
GUANGZHOU XIAOPENG CONNECTIVITY TECH CO LTD
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Problems solved by technology

However, in practice, it is found that the current feature alignment method based on the image processing model has a large amount of

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  • Image processing method and device, electronic equipment and storage medium
  • Image processing method and device, electronic equipment and storage medium
  • Image processing method and device, electronic equipment and storage medium

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[0050]下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。

[0051]需要说明的是，本申请实施例及附图中的术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。

[0052]本申请实施例公开了一种图像处理方法、装置、电子设备及存储介质，能够减少图像处理模型进行特征对齐操作时的计算量，从而降低模型的学习难度，提高特征对齐的准确性。以下分别进行详细说明。

[0053]首先，对本申请实施例公开的图像处理模型进行介绍。本申请实施例公开的图像处理模型可包括对齐模块与任意一种现有的神经网络模块。对齐模块用于对当前帧和过去帧进行特征对齐，当前帧和过去帧可以是车辆的摄像装置拍摄到的任意两帧在时序上存在关联的图像帧，过去帧在时序上处于当前帧之前。

[0054]在一些实施例中，上述的任意一种现有的神经网络模块可包括时序循环模块与延迟模块。时序循环模块可以是RNN、LSTM、GRU等网络模块或者他们的变体，延迟模块可用于将输入的图像帧延迟一个或多个时间步的步长之后输出。也就是说，本申请实施例公开的图像处理模型可以在现有的神经网络结构上额外增加一个对齐模块，对齐模块可插入任意一种现有的视频检测、目标识别模型，不需要修改已有神经网络模型的大部分模型结构。

[0055]示例性的，请参阅图1A，图1A是一个实施例公开的一种经典循环模块的结构示意图。如图1A所示，经典循环结构可包括延迟模块110和时序循环模块120。

[0056]其中，图1A所示的xt是t时刻的特征图，ht-1是t-1时刻的特征图，t可为大于或等于1的正整数。即，xt可作为当前帧，ht-1可作为过去帧，可以是延迟单元110输出的。

[0057]在经典循环模块中，当前帧xt和过去帧ht-1可以输入至时序循环模块120中，由时序循环模型120对当前帧xt和过去帧ht-1进行目标检测、目标提取、目标识别等一种或多种图像处理操作，并输出特...

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Abstract

The embodiment of the invention discloses an image processing method and device, electronic equipment and a storage medium. The method comprises the steps that a current frame and a past frame are input to an alignment module; the past frame is an image frame before the current frame in time sequence; the alignment module is a processing module in an image processing model, and the image processing model is obtained by training sample data; extracting, by the alignment module, a first foreground region of the past frame; determining a movement offset of the first foreground region relative to the current frame through the alignment module; and performing position transfer processing on the first foreground region according to the movement offset through the alignment module to obtain a second foreground region aligned with the current frame position. By implementing the embodiment of the invention, the calculation amount when the image processing model performs feature alignment operation can be reduced, so that the learning difficulty of the model is reduced, and the accuracy of feature alignment is improved.

Description

technical field [0001] The present application relates to the technical field of image processing, and specifically relates to an image processing method, device, electronic equipment, and storage medium. Background technique [0002] Vision-based autonomous driving solutions often need to process the images captured by the vehicle to convert the information in the images into data that the vehicle's processor can understand. Generally, the vehicle continuously shoots the surrounding road conditions while driving, so both the foreground area and the background area in the captured image will show a state of motion. At this time, it is generally necessary to perform feature alignment on moving objects in consecutive images, so as to facilitate subsequent image processing operations. [0003] In the prior art, image processing models such as neural networks and classifiers can be trained by machine learning methods, and the trained models are used to perform feature alignment...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/215G06T7/194G06T7/38
CPCG06T7/248G06T7/38G06T7/215G06T7/194G06T2207/30252
Inventor 徐兴伟
Owner GUANGZHOU XIAOPENG CONNECTIVITY TECH CO LTD
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