Computer Vision (CV) has come a long way in recent years, permeating various aspects of our daily lives. While it may seem like a groundbreaking innovation to the average person, the truth is that CV has been evolving for decades. The studies conducted in the 1970s laid the early foundations for many of the algorithms that are still in use today. However, around a decade ago, a new technique emerged on the scene: Deep learning. This form of artificial intelligence (AI) utilizes neural networks to solve incredibly complex problems, provided there is sufficient data and computational power available.
Deep learning quickly gained momentum as a powerful tool in CV. It proved particularly effective in solving challenges such as object detection and classification. The distinction between “classical” CV, which relied on mathematical problem-solving, and deep learning-based CV began to take shape. However, it is important to note that deep learning did not render classical CV obsolete. Both approaches continued to evolve, shedding new light on which challenges are best solved through big data and which should still be tackled using mathematical and geometric algorithms.
Deep learning, specifically convolutional neural networks (CNNs) and region-based CNNs (R-CNNs), revolutionized object detection in CV. With access to massive labeled image databases and a well-trained network, explicit, handcrafted rules were no longer necessary. These algorithms became adept at detecting objects under various circumstances, regardless of angle. Feature extraction also benefited from deep learning, requiring only a competent algorithm and diverse training data to prevent overfitting and achieve high accuracy ratings.
Semantic segmentation, a process of labeling each pixel within an image, was once a tedious task in classical CV. Deep learning, specifically the utilization of U-net architecture, has shown exceptional performance in this area. It eliminates the need for complex manual processes and streamlines the segmentation process. This breakthrough has significantly enhanced the efficiency and effectiveness of semantic segmentation tasks.
While deep learning has undoubtedly revolutionized CV, classical CV approaches still outperform newer techniques in simultaneous localization and mapping (SLAM) and structure from motion (SFM) algorithms. SLAM involves building and updating maps of physical areas while keeping track of the agent’s position within the map. SFM, on the other hand, aims to create a 3D reconstruction of an object using multiple views. These tasks heavily rely on advanced mathematics and geometry. Classical CV solutions excel in these areas, proving more effective and offering a cost-effective alternative to laser scanning.
It is crucial to recognize that there are still certain problems that deep learning cannot solve as effectively as classical CV. In situations involving complex math, direct observation, and the unavailability of a suitable training dataset, deep learning may not be the most elegant solution. The transition from classical to deep learning-based CV should be approached with caution, as wholesale replacement is not always the best approach. Instead, a case-by-case analysis is necessary to determine which problems will benefit from the new techniques and which are better suited to traditional approaches.
While the adoption of deep learning has brought advancements to the CV industry, it also marks a shift away from the artistic and creative elements of classical CV. Classical methodologies relied on the creativity and innovation of engineers to extract features, identify objects, and understand key elements within an image. Deep learning, in contrast, leans more towards data-driven analysis. This transition presents a challenge for engineers to incorporate artistry and creativity in other ways and strike a balance between the two approaches.
Looking ahead to the next decade, the focus in CV network development is likely to shift from “learning” to “understanding.” The aim will no longer be solely on how much a network can learn, but rather on enabling it to comprehend information deeply with minimal intervention. This shift will facilitate more profound conclusions and insights while minimizing the dependence on excessive data. The future of CV holds many surprises, and it is possible that classical CV may eventually become obsolete or be replaced by yet-unheard-of techniques. However, for now, both classical CV and deep learning-based approaches are the best options for specific tasks and will play a vital role in the progression of CV in the coming years.
Computer vision has experienced a significant evolution, driven by the integration of deep learning and classical CV techniques. Deep learning has excelled in areas such as object detection and feature extraction, while classical CV still outperforms in SLAM and SFM algorithms. A balanced approach, considering the strengths and limitations of both approaches, is crucial for addressing the diverse challenges in computer vision. As the field continues to advance, it is essential to preserve the artistry and creativity of classical CV while embracing the scalability and efficiency of deep learning. The journey of computer vision promises to be exciting and full of discoveries, providing innovative solutions to complex problems in various industries.