Target detection method, system and apparatus for object with large length-width ratio, and medium

A target detection, aspect ratio technology, applied in image data processing, instrument, character and pattern recognition, etc., can solve the problems of IoU loss, inaccurate target position, error, etc., to improve accuracy and efficiency, and improve detection performance Effect

Active Publication Date: 2021-08-13
杭州鄂达精密机电科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In some cases, the network cannot detect objects with a large aspect ratio. In other cases, although the network can detect objects with a large aspect ratio, the target position is not accurate enough, especially the

Method used

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  • Target detection method, system and apparatus for object with large length-width ratio, and medium
  • Target detection method, system and apparatus for object with large length-width ratio, and medium
  • Target detection method, system and apparatus for object with large length-width ratio, and medium

Examples

Experimental program
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Example Embodiment

[0058] Example 1:

[0059] This embodiment implements a target detection method for objects with a large aspect ratio, such as figure 1 shown, including the following steps:

[0060] S101. Obtain a target image to be detected;

[0061] S102. Input the target image to be detected into the trained target detection model, wherein the loss function of the trained target detection model includes the length loss value of the long side of the object and the center position loss value of the long side of the object;

[0062] S103. Output the position of each object in the target image and a plurality of category probability values;

[0063] S104. Determine the final category of each object according to the plurality of category probability values.

[0064] Specifically, the loss function of the target detection model includes the length loss value of the long side of the object and the center position loss value of the long side of the object, including: judging whether the object ...

Example Embodiment

[0070] Example 2:

[0071] This embodiment implements a target detection method for an object with a large aspect ratio, including four steps, and the specific steps are described in detail as follows.

[0072] The first step is to obtain the target image to be detected.

[0073] Preferably, the acquired image of the target to be detected includes objects with a large aspect ratio, such as skis, pencils and the like.

[0074] The second step is to input the target image to be detected into the trained target detection model, wherein the loss function of the trained target detection model includes the length loss value of the long side of the object and the center position loss of the long side of the object value.

[0075] Specifically, the loss function of the target detection model includes the length loss value of the long side of the object and the center position loss value of the long side of the object, including: judging whether the object is an object with a large a...

Example Embodiment

[0086] Example 3:

[0087] This embodiment implements a target detection method system for objects with a large aspect ratio, such as image 3 shown, including:

[0088] An image acquisition module 301, configured to acquire a target image to be detected;

[0089] An image input module 302, configured to input the target image to be detected into the trained target detection model, wherein the loss function of the trained target detection model includes the length loss value of the long side of the object and the length of the long side of the object Center position loss value;

[0090] An output module 303, configured to output the position of each object in the target image and a plurality of category probability values;

[0091] A final category determining module 304, configured to determine the final category of each object according to the plurality of category probability values.

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Abstract

The invention relates to the technical field of target detection, in particular to a target detection method, system and apparatus for an object with a large length-width ratio, and a medium. The method comprises the following steps: acquiring a to-be-detected target image; inputting the to-be-detected target image into a trained target detection model, wherein a loss function of the trained target detection model comprises a length loss value of a long side of an object and a central position loss value of the long side of the object; outputting the position of each object in the target image and a plurality of category probability values; and determining a final category of each object according to the plurality of category probability values. According to the target detection method for the object with the large length-width ratio, the central position loss value of the long side of the object and the length loss value of the long side of the object are additionally added into the loss function, so that the model is guided to improve the detection performance of the object with the large length-width ratio, and the target detection precision and efficiency are improved.

Description

technical field [0001] The present application relates to the technical field of target detection, and more specifically, the present application relates to a target detection method, system, device and medium for objects with a large aspect ratio. Background technique [0002] Object detection is an important research direction of computer vision and digital image processing, and it is widely used in robot navigation, intelligent video surveillance, industrial inspection, aerospace and many other fields. The goal of object detection is to find the object of interest in the image, including two subtasks of object location and object classification, that is, to determine the category and location of the object at the same time. [0003] At present, the target detection mode that uses neural networks combined with a large amount of image data for training has become the mainstream method in the industry. Algorithms based on neural networks can basically be classified into two...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06T7/60
CPCG06T7/60G06V10/25G06F18/2415
Inventor 廖丹萍
Owner 杭州鄂达精密机电科技有限公司
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