<p>Variations in lighting conditions significantly impact the accuracy of object detection in computer vision applications, particularly when relying on edge detection techniques. This paper presents a comprehensive review of the complexities and challenges associated with edge detection with respect to varying illumination. We analyze the effects of lighting fluctuations on edge detection and explore a wide range of applications where a thorough understanding of light changes is crucial for accurate object localization. Furthermore, this work offers an extensive survey of traditional and deep-learning-based edge detection methods, with a particular focus on techniques that effectively address illumination variations. We also explore commonly used datasets, metrics, and measures for evaluating edge detection performance. Additionally, we delve into current challenges in edge detection, proposing potential future research directions to bridge the gap between automated methods and human visual perception. This comprehensive review aims to contribute to the advancement of edge detection by providing valuable insights and guidance for researchers and practitioners in the field.</p>