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.