Abstract: Aiming at the problem of low accuracy and poor effect of traditional target contour recognition algorithm for image target contour recognition, the author proposed a binary image target contour recognition algorithm based on deep learning. Firstly, the deep convolution network algorithm in the
deep learning algorithm was selected to recognize the target contour of binary image, and the binary image was divided into non overlapping sub block images with the same size and input into the first layer of the deep convolution network. Secondly, the filter (convolution kernel) in the convolution network used the cost function optimized by the traditional neural network algorithm to implement convolution filtering on the input sub block image, and sent the down sampling image after convolution filtering to the second layer. After the same processing, the second layer input the result into the third layer, and the output image of the third layer was the target contour recognition result of the sub block. Finally, after all the sub blocks were recognized, all the sub blocks were clustered in the output layer by the full connection method, and the final binary image target contour recognition results were output. The experimental results show that the average recognition accuracy of 15 binary images is 98.75%, the average signal-to-noise ratio is 2.42, and the recognition effect is better.

Key words: deep learning, binary image, target, contour, recognition algorithm