This article discusses the comparison between Deep Learning (DL) and traditional computer vision (CV) techniques, highlighting their respective advantages and limitations. While DL has significantly advanced the field of digital image processing, traditional CV methods still hold value and should not be disregarded. The paper aims to encourage a discussion on whether knowledge of classical CV techniques should be maintained and explores how the two approaches can be combined. Deep Learning, a subset of machine learning, uses artificial neural networks to learn patterns from data. It has revolutionized image processing, achieving high accuracy in tasks like image classification, segmentation, and object detection. However, traditional CV techniques, such as feature extraction using SIFT, SURF, and other algorithms, have their own strengths, particularly in scenarios with limited data or where transparency and interpretability are crucial. The paper emphasizes that DL is not a universal solution and that there are problems where traditional CV methods are more suitable. For example, in applications like 3D vision and panoramic imaging, where DL models have not yet been fully optimized, traditional techniques can offer better performance. Hybrid approaches that combine DL with traditional CV methods have shown promise in improving performance and addressing specific challenges. The article also highlights the importance of understanding both approaches. While DL offers greater accuracy and versatility, it requires substantial computational resources and large datasets. Traditional CV techniques, on the other hand, are often more efficient and transparent. The paper suggests that integrating traditional CV with DL can lead to more robust and efficient solutions, especially in edge computing and resource-constrained environments. In conclusion, the paper argues that traditional CV techniques remain relevant and valuable, even in the age of DL. It encourages the continued study and application of classical CV methods, particularly in areas where they can complement or enhance DL approaches. The paper also emphasizes the need for further research into hybrid methodologies to leverage the strengths of both DL and traditional CV techniques.This article discusses the comparison between Deep Learning (DL) and traditional computer vision (CV) techniques, highlighting their respective advantages and limitations. While DL has significantly advanced the field of digital image processing, traditional CV methods still hold value and should not be disregarded. The paper aims to encourage a discussion on whether knowledge of classical CV techniques should be maintained and explores how the two approaches can be combined. Deep Learning, a subset of machine learning, uses artificial neural networks to learn patterns from data. It has revolutionized image processing, achieving high accuracy in tasks like image classification, segmentation, and object detection. However, traditional CV techniques, such as feature extraction using SIFT, SURF, and other algorithms, have their own strengths, particularly in scenarios with limited data or where transparency and interpretability are crucial. The paper emphasizes that DL is not a universal solution and that there are problems where traditional CV methods are more suitable. For example, in applications like 3D vision and panoramic imaging, where DL models have not yet been fully optimized, traditional techniques can offer better performance. Hybrid approaches that combine DL with traditional CV methods have shown promise in improving performance and addressing specific challenges. The article also highlights the importance of understanding both approaches. While DL offers greater accuracy and versatility, it requires substantial computational resources and large datasets. Traditional CV techniques, on the other hand, are often more efficient and transparent. The paper suggests that integrating traditional CV with DL can lead to more robust and efficient solutions, especially in edge computing and resource-constrained environments. In conclusion, the paper argues that traditional CV techniques remain relevant and valuable, even in the age of DL. It encourages the continued study and application of classical CV methods, particularly in areas where they can complement or enhance DL approaches. The paper also emphasizes the need for further research into hybrid methodologies to leverage the strengths of both DL and traditional CV techniques.