Method and device for detecting target object

Abstract

Embodiments of the invention provide a method and a device for detecting a target object. The method comprises the steps of acquiring a target detection model, wherein the target detection model is obtained by training supervised labeling data and unsupervised labeling data in training image data, the target detection model is a detection model used for indicating the target object; and the supervised labeling data and the unsupervised labeling data are used for indicating attribute information of the target object in the training image data; determining to-be-detected image data; and detecting the to-be-detected image data through the target detection model, and outputting the attribute information of the target object in the to-be-detected image data. According to the method and the device for detecting the target object provided by the embodiments of the invention, not only the detection efficiency of the target object but also the detection accuracy of the target object are improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to a method and device for detecting a target object. Background technique [0002] Object detection is an important research direction in the field of image processing technology. Before performing target object detection, it is necessary to obtain a target detection model, and then use the target detection model to detect the target in the image. In the existing technology, it is usually necessary to first filter and label the original data through manual labeling methods to obtain manually labeled data, and then train these manually labeled data to obtain a target detection model, and then use the trained target detection model The model detects the target objects in the real environment. [0003] However, the manual labeling method not only reduces the detection efficiency of the target object, but also may cause the quality of the image tar...

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Problems solved by technology

[0003] However, the manual labeling method not only reduces the detection efficiency of the target object, but also may cause the quality of the image target detection model trained using these manual labeling data to be low due to the different labeling criteria used by the labelers, making it impossible to accurately detect the image target object, resulting in low accuracy of target object detection

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