Steel coil end face edge detection algorithm based on deep learning

Abstract

The invention discloses a steel coil end face edge detection algorithm based on deep learning. The method comprises the steps of improving a deep convolutional neural network structure; a multi-scalefeature extraction part and a multi-scale feature fusion part are improved respectively; wherein the multi-scale feature extraction part is composed of five feature extraction modules with different scales, the multi-scale feature fusion part is mainly composed of a convolution layer and a deconvolution layer, image acquisition is carried out, an acquired image is sent to a deep convolutional neural network for edge detection, and then an edge detection result is output; according to the method, the deep convolutional neural network is improved, the deep convolutional neural network automatically learns the features from the data, the edge neglecting the numbers can be learned, only the edge of the steel coil is expressed, the robustness is better, and the detection effect is good.

Description

technical field [0001] The invention relates to the technical field of industrial vision, in particular to an edge detection algorithm of a steel coil end face based on deep learning. Background technique [0002] The end face of the steel coil has strong regular texture edge features. In order to automatically detect the defect of the end face of the steel coil, the first step is to detect the edge of the end face of the steel coil to provide auxiliary information for subsequent defect detection. [0003] In the prior art, edge detection algorithms based on traditional image processing often start from the perspective of calculating image pixel gradients, and use various edge detection operators to perform sliding window calculations on images through convolution filtering. Among them, the most commonly used It is the canny edge detection algorithm. However, when applied to the edge detection field of steel coil end faces, the edge images obtained based on traditional imag...

AI Technical Summary

Problems solved by technology

However, when applied to the edge detection field of steel coil end faces, the edge images obtained based on traditional image processing algorithms often have many noise points, such as highlights and shadows generated by light, and uneven parts caused by oxidation on the surface of steel coils, etc. The edge image detected by the traditional algorithm produces a lot of interference, and the loss function of the traditional edge detection uses a pixel-by-pixel binary cross-entropy loss function, and then sums the pixel-by-pixel loss values ​​to obtain the final loss value, which is Since the pixels occupied by the edge lines are often very few, and the background pixels are particularly large, the imbalance between the two categories will cause the network to easily fall into the local extremum during the training process, resulting in low accuracy of the calculation results.

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