Recent Trends in Edge Detection: Comparative Analysis of Classical Operators and Modern Vision Techniques

This study highlights the progression from classical edge detection operators to modern vision-driven techniques. Classical methods such as Roberts, Prewitt, and Sobel provide simple baselines but suffer from noise sensitivity and fragmented edges. Second-order approaches like LoG and DoG improve detail detection yet remain prone to false edges. Modern algorithms—Canny, Deriche, and SUSAN—deliver superior accuracy, robustness, and edge continuity, making them more effective for complex and real-time applications. Among these, the Canny edge detector emerges as the best-performing method, consistently achieving the highest accuracy and producing thin, continuous edges with strong noise suppression [2]. Overall, the transition toward hybrid and AI-integrated approaches underscores their growing relevance in medical imaging, biometric identification, autonomous navigation, and next-generation computer vision systems [3][6].

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