Edge detection requires precise low-level details and a thorough understanding of high-level semantics. Existing approaches either patch Convolutional Neural Networks (CNNs) to integrate global context or supplement Transformers to capture local cues, with unsatisfactory performance. In this paper, we introduce HCTEdge, a novel hybrid CNN-Transformer architecture to effectively capture both local details and global context. HCTEdge incorporates a global context enhancement (GCE) module and a channel attention fusion (CAF) module to enhance feature extraction and multi-scale feature fusion. To further enhance the system’s overall performance and adaptability, a hierarchical aggregation (HA) decoder is proposed. Extensive experiments conducted on four widely used datasets demonstrate that HCTEdge offers substantial improvements in both precision and clarity. Compared to state-of-the-art methods, HCTEdge achieves a remarkable 1% increase in the optimal image scale (OIS). Qualitative results validate HCTEdge’s superior semantic understanding and outstanding detail extraction capabilities. Our code is available at https://anonymous.4open.science/r/HCTEdge-E423 .