Deep Hough Transform for Semantic Line Detection

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 9; pp. 4793 - 4806
• Main Authors: Zhao, Kai, Han, Qi, Zhang, Chang-Bin, Xu, Jun, Cheng, Ming-Ming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: CNN; Datasets; deep learning; Detectors; Domains; Feature extraction; Feature maps; hough transform; Hough transformation; Image edge detection; Measurement; Object recognition; Quality assessment; Representations; Semantic line detection; Semantics; Source code; Task analysis; Transforms
• DOI: 10.1109/TPAMI.2021.3077129
• ISSN: 0162-8828; 1939-3539

We focus on a fundamental task of detecting meaningful line structures, a.k.a. , semantic line, in natural scenes. Many previous methods regard this problem as a special case of object detection and adjust existing object detectors for semantic line detection. However, these methods neglect the inherent characteristics of lines, leading to sub-optimal performance. Lines enjoy much simpler geometric property than complex objects and thus can be compactly parameterized by a few arguments. To better exploit the property of lines, in this paper, we incorporate the classical Hough transform technique into deeply learned representations and propose a one-shot end-to-end learning framework for line detection. By parameterizing lines with slopes and biases, we perform Hough transform to translate deep representations into the parametric domain, in which we perform line detection. Specifically, we aggregate features along candidate lines on the feature map plane and then assign the aggregated features to corresponding locations in the parametric domain. Consequently, the problem of detecting semantic lines in the spatial domain is transformed into spotting individual points in the parametric domain, making the post-processing steps, i.e., non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features that are critical for accurate line detection. In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task. Experimental results on our proposed dataset and another public dataset demonstrate the advantages of our method over previous state-of-the-art alternatives. The dataset and source code is available at https://mmcheng.net/dhtline/ .