Deep Learning (DL) has significantly advanced Digital Image Processing, but traditional computer vision (CV) techniques remain relevant. This paper compares DL and traditional CV, highlighting their strengths and weaknesses. DL excels in tasks like image classification, segmentation, and object detection, offering high accuracy with minimal human intervention. However, traditional CV methods are still valuable for specific applications, such as 3D vision, panoramic stitching, and robotics, where DL may not perform optimally. Hybrid approaches combining DL and traditional CV can enhance performance, especially in resource-constrained environments. DL requires substantial computational power and data, while traditional CV offers transparency and efficiency. Challenges include overfitting, data scarcity, and the need for domain-specific knowledge. Edge computing and hybrid models are increasingly used to address these issues. 3D vision, SLAM, and panoramic stitching are areas where traditional CV techniques remain crucial. The paper emphasizes that while DL is powerful, traditional CV methods are still necessary for certain tasks, and their integration can lead to more robust solutions. The future of CV likely involves a blend of DL and traditional techniques, leveraging the strengths of both to tackle complex problems.Deep Learning (DL) has significantly advanced Digital Image Processing, but traditional computer vision (CV) techniques remain relevant. This paper compares DL and traditional CV, highlighting their strengths and weaknesses. DL excels in tasks like image classification, segmentation, and object detection, offering high accuracy with minimal human intervention. However, traditional CV methods are still valuable for specific applications, such as 3D vision, panoramic stitching, and robotics, where DL may not perform optimally. Hybrid approaches combining DL and traditional CV can enhance performance, especially in resource-constrained environments. DL requires substantial computational power and data, while traditional CV offers transparency and efficiency. Challenges include overfitting, data scarcity, and the need for domain-specific knowledge. Edge computing and hybrid models are increasingly used to address these issues. 3D vision, SLAM, and panoramic stitching are areas where traditional CV techniques remain crucial. The paper emphasizes that while DL is powerful, traditional CV methods are still necessary for certain tasks, and their integration can lead to more robust solutions. The future of CV likely involves a blend of DL and traditional techniques, leveraging the strengths of both to tackle complex problems.