Semantic Line Detection Using Deep-Hough with Attention Mechanism and Strip Convolution

Semantic lines are some particular dominant lines in an image, which divide the image into several semantic regions and outline its conceptual structure. They play a vital role in image analysis, scene understanding, and other downstream tasks due to their semantic representation ability for the image layout. However, the accuracy and efficiency of existing semantic line detection methods still couldn’t meet the need of real applications. So, a new semantic line detection method based on the deep Hough transform network with attention mechanism and strip convolution is proposed. Firstly, the detection performance is improved by combining the channel attention mechanism with the feature pyramid network to alleviate the influence of redundant information. Then, the strip convolution and mixed pooling layer are introduced to effectively collect the remote information and capture the long-range dependencies between pixel backgrounds. Finally, the strategy of GhostNet is adopted to reduce the computational cost. Results of experiments on open datasets validate the proposed method, which is comparable to and even outperforms the state-of-the-art methods in accuracy and efficiency. Our code and pretrained models are available at: https://github.com/zhizhz/sml.