Deep Learning-Based Feature Detection Vs Classical Methods: a Comparative Case Study In Computer Vision

Abstract

Feature detection is a fundamental task in computer vision enabling image matching, object recognition, visual SLAM, and 3D reconstruction. Classical algorithms such as the Harris Corner Detector and SIFT rely on hand-crafted mathematical formulations grounded in image gradient analysis and scale-space theory. While effective in controlled environments, these methods degrade under real-world variation in illumination, scale, viewpoint, noise, and motion blur. CNN-based detectors such as SuperPoint learn feature representations directly from data, achieving superior accuracy, robustness, and scalability. This paper provides a comprehensive comparative analysis supported by key mathematical formulations, performance metrics, and industrial applications, demonstrating that deep learning methods consistently outperform classical approaches under challenging conditions.

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