<p>Edges are crucial for higher-level visual tasks, such as image segmentation and object recognition. The main challenge of edge detection lies in preserving object contours while suppressing texture edges. To address this challenge, existing bio-inspired edge detection methods introduce various surround modulation mechanisms to suppress texture edges. However, these methods have not considered incorporating valuable edge cues, such as texture boundaries to enhance object contours. In this paper, we integrate texture gradients into a bio-inspired hierarchical edge detection model. By combining texture gradients with edge responses regulated by a new surround modulation mechanism, our model can better detect significant edges. Moreover, the endpoint cells in V2, which respond to endpoints while weakly or not responding to edges, inspire us to model them to inhibit edge responses. We conduct experiments on two of the most widely used benchmarks (BSDS500 and MBDD). The results show that our method achieves better performance compared with other bio-inspired methods, with a 2% improvement in ODS F-score on BSDS500 and a 1% improvement on MBDD. Applying our method to line segment detection also demonstrates that it achieves results comparable to other leading methods.</p>