Refined edge detection with cascaded and high-resolution convolutional network

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with traditional methods like Sobel or Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results with erroneous edges. In this paper, we attempted to overcome these challenges for refined edge detection using a cascaded and high-resolution network named (CHRNet). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, sub-blocks are connected at every stage with the output of the previous layer. Also, after each layer, we use batch normalization layer with an active affine parameter as an erosion operation for the homogeneous region in the image. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.The code is available at: https://github.com/elharroussomar/chrnet/