A target detection method, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the field of image processing, and discloses a target detection method, electronic equipment and a storage medium. In some embodiments of the present application, the target detection method includes: acquiring the image to be detected; calling the target detection model to detect the image to be detected, determining the type of the target object in the image to be detected, and the target detection model according to the first image data and the first The category labeling file of the image data is trained, and the classification model corresponding to the category of the target object is called to identify the category of the target object; the classification model is trained according to the second image data and the type information of the object in the second image data, and the second image data Include images of objects of the category corresponding to the classification model. In this implementation, the detection speed can be increased.

Description

technical field [0001] Embodiments of the present invention relate to the field of image processing, and in particular, to a target detection method, electronic equipment, and a storage medium. Background technique [0002] Deep learning is a method based on representation learning of data in machine learning. The advantage of deep learning is to use unsupervised or semi-supervised feature learning and hierarchical feature extraction efficient algorithms to replace manual feature acquisition. At present, when a smart container recognizes goods, it is often based on a deep learning target detection algorithm to detect and identify the goods in the container. Using deep learning target detection algorithm is more accurate than traditional target detection algorithm. [0003] However, the inventors have found that there are at least the following problems in the prior art: when using a deep learning detection algorithm to train a target detection model, the training time will...

AI Technical Summary

Problems solved by technology

[0003] However, the inventors have found that there are at least the following problems in the prior art: when using a deep learning detection algorithm to train a target detection model, the training time will increase with the increase in the amount of training data, which is compared with traditional target detection algorithms. an obvious shortcoming

Therefore, it often takes a long time for the target detection model to be able to detect new objects

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