Edge detection plays a critical role in computer vision, particularly in the analysis of random natural images. It serves as a fundamental step in tasks such as image segmentation, shape extraction, pattern recognition, auto-navigation, and motion analysis, with applications spanning various domains including radar and sonar image processing. The edge detection model attempts to identify points in digital images where significant intensity changes occur, known as edges or region boundaries. Traditionally, edge detection relied on gradient-based operators, which often produced jagged edges and were susceptible to image noise. In recent years, the emergence of deep learning technology has revolutionized this field by utilizing its ability to automatically learn complex features from natural images. Deep learning approaches offer significant advantages in capturing high-level representations, thereby improving the accuracy and robustness of edge detection algorithms. Moreover, the effectiveness of edge detection techniques varies depending on the content and classification of images, such as natural scenes, medical images, or underwater environments. This study aims to evaluate and compare the performance of five widely used deep learning-based edge detection methods to identify the most effective approach specifically tailored for natural images. Through comprehensive experimentation and analysis, this research contributes to advancing the state-of-the-art in edge detection for random natural images, providing insights into the optimal application of deep learning techniques in this domain.