Autonomous driving-study notes for rectangular frame detection

1 Acknowledgements

Thank you Zhixing for your code and guidance!

2 The basic process of rectangular frame detection

1. According to the characteristics of the image, perform grayscale according to the channel value. Here we choose the two channels [B, R];

(We do not perform Gaussian blur here, because the noise of the image is relatively small; the main reason is: the edge of the target rectangle is relatively thin)

2. Use the open operation to obtain the gradient map;

2.1 Use the open operation to obtain the gradient map

Through experiments, we can know the degree of "blooming" of the image determined by the kernel_size of the operation,

(The larger the kernel_size, the larger the range of the ink halo effect, and it seems "seems to be more blurred")

This is the effect of setting the kernel_size to 30. You can see that the blooming is already very serious, but the edge information will still be retained;

It can be seen that the areas in the middle are all connected into one piece, so that a lot of fine-grained edge texture information will be lost, so that the fine-grained texture information cannot be highlighted;

Since the edges of the target rectangular box are relatively thin here, we set kernel_size to 2 ;

Guess you like

Origin blog.csdn.net/songyuc/article/details/107159191