基于CNN跨层融合结构的边缘检测算法

传统边缘检测算法难以处理复杂的图像, 而现有基于深度的边缘检测模型, 其检测结果往往存在边缘定位错误和信息丢失等现象. 针对此类问题, 提出一种基于RCF的高精度的边缘检测算法RCF-CLF. 首先, 引入HDC结构设计用于避免因叠加相同膨胀卷积而引起的网格效应; 其次, 设计了一种特征增强结构, 旨在融合多尺度信息、扩大感受野; 然后, 设计了跨层融合结构, 将高层信息和低层信息融合, 用于提取准确的边缘信息; 最后, 引入注意力机制CBAM, 通过聚焦物体边缘区域, 抑制非边缘区域, 从而提高网络对边缘信息的提取能力. 本文在BSDS500和BIPED数据集上评估所提出的方法, 与RCF算法相比, 在BIPED数据集上, 主要指标ODS、OIS和AP分别达到了0.893、0.901和0.945, 提高了近5个百分点, 在BSDS500数据集上, 主要指标也有所提升. 此外, 与其他同类算法相比, 本文算法也具有一定的优势, 可以实现更加准确的边缘定位.

Edge Detection Algorithm Based on CNN Cross-layer Fusion Structure

Traditional edge detection algorithms are difficult to deal with complex images, and the existing depth-based edge detection models often have edge positioning errors and information loss in the detection results. Aiming at such problems, this study proposes a high-precision edge detection algorithm RCF-CLF based on RCF. First, the HDC structure is introduced to avoid the grid effect caused by superimposing the same dilated convolution. Second, a feature enhancement structure is designed to fuse multi-scale information and expand the receptive field. Then, a cross-layer fusion structure is designed, which integrates high-level and low-level information to extract accurate edge information. Finally, the attention mechanism CBAM is introduced to focus on the edge area of the object and suppress the non-edge area, thereby improving the ability of the network to extract edge information. This study evaluates the proposed method on the BSDS500 and BIPED datasets. Compared with the RCF algorithm, the main indicators ODS, OIS, and AP reached 0.893, 0.901, and 0.945, respectively, with an increase of nearly 5 percentage points on the BIPED dataset. On the BSDS500 dataset, the main indicators have also improved. In addition, compared with other similar algorithms, the proposed algorithm also has certain advantages, which can achieve more accurate edge positioning.