Edge Detection Algorithm for Urban Road Images Based on Deep Learning

Conventional algorithms demonstrate poor performance in complex urban road settings. Factors like noise, shadows, and changes in the composition of the road surface in photos can erode the accuracy of traditional algorithms, making them less reliable in actual city settings. The accuracy of edge detection in photographs of urban roads is improved by the incorporation of an effective neural network. This enhancement makes it possible for the algorithm to more accurately capture the intricate details and geometric structure of highways. In order to preserve the original image quality, this study preprocesses a sizable amount of urban road image data. The establishment of an efficient neural network involves constructing a network with an Encoder for image downsampling. The bottleneck comprises multiple residual blocks, and an Encoder is employed for image upsampling.