An industrial product image angle detection and correction method based on deep learning

By constructing a deep learning neural network model for detecting the angle of industrial product images, the problem of the inability to effectively correct the angle of industrial product images in existing technologies is solved, achieving detection and correction over a wider angle range and improving the robustness and detection speed of the model.

CN115511827BActive Publication Date: 2026-05-26BEIJING DAHENG IMAGE VISION CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING DAHENG IMAGE VISION CO LTD
Filing Date
2022-09-26
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing angle detection methods cannot effectively correct angles based on the texture of industrial product images, and cannot determine the specific angle information of the target, especially in complex backgrounds where background interference problems have not been effectively solved.

Method used

A neural network model for angle detection of industrial product images based on deep learning is constructed. Angle detection and correction are achieved by combining feature extraction, edge detection, feature fusion and attention mechanism with loss function optimization.

Benefits of technology

The angle prediction range was extended to [-180°, 180°), eliminating the periodic abrupt changes in angle loss, improving the robustness of the model and the ability to extract texture features, reducing the number of steps in object detection, and improving detection speed and resistance to interference from complex backgrounds.

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Abstract

This invention relates to a deep learning-based method for angle detection and correction of industrial product images, comprising the following steps: Step 1, acquiring industrial product image information; Step 2, building an industrial product image angle detection neural network model and training the network model using the industrial product image information; Step 3, after the neural network model training is completed, loading the trained model parameters, obtaining the result feature map through model forward operation, restoring it according to the labeled format to obtain the network model prediction result; Step 4, correcting the angle of the industrial product image according to the target position and angle predicted by the neural network model, and correcting the detected industrial product image angle to a uniform orientation.
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