What Are the Top 5 Challenges to Achieve Computer Vision?

 

Computer vision technology helps modern businesses solve a range of complex visual tasks, such as defect detection, self-driving cars, and medical imaging, among others. It has made an impact across sectors, including automotive, retail, healthcare, and others. According to Grand View Research, the market for computer vision is expected to be worth $19.1 billion by 2027, at a CAGR of 7.6%. It is a field of study that encompasses developing algorithms and techniques for interpreting and analyzing visual data from the world around us. This is where data labeling comes into play to train machine learning models for tasks related to computer vision. These models need to be provided with a large amount of data in the form of images or videos that are labeled with annotations.

The annotations describe the features present in the data. For instance, solutions to infuse artificial vision in computer systems depend on data annotation, as in the data labeling platform Zastra. Further, data labeling deals with tagging images or videos (manually or automatically) with labels that may include information such as the size, position, and shape of objects, image quality, lighting conditions, and other environmental factors. Data labeling is an integral part of computer vision, for it provides the required training data for machine learning models to identify patterns and make predictions.

However, for data scientists dealing with computer vision, there are challenges galore. The article discusses five such challenges, as mentioned below:

Top 5 Challenges to be Addressed by Every Data Scientist

Computer vision enables computer systems to see and analyze the world as humans do. Soon, it is likely to become mainstream. The technology seems to be emulating the human visual system to perform automation tasks where visual cognition is important, say, driving automated cars. At the same time, the process of deciphering images is more complicated than merely analyzing data. This is due to the presence of a vast amount of data in multi-dimensional form to be analyzed in an image. Deep learning and artificial neural networks are used to enhance the capabilities of computer vision and replicate human vision. However, without data labeling, algorithms for computer vision may not be able to detect objects or features in images or videos accurately. This, in turn, can lead to inaccurate results and poor performance.

1. Object Recognition and Detection: This refers to the ability of a computer system to detect and locate objects in an image or video using data annotation and labeling. It enables machines to perceive the world around them using a data labeling platform while being used for various applications. These may include autonomous vehicles, augmented reality, security systems, and others. As one of the fundamental challenges in computer vision, object recognition, and detection get complicated by the fact that objects can appear at different scales, orientations, and lighting conditions and may be partially occluded by other objects in the scene.

2. Image Segmentation: The process involves dividing an image into multiple regions or segments that are related to different objects or regions in the scene. It aims at simplifying or changing the representation of an image and making it easier to analyze. Image segmentation allows computer systems to analyze the contents of an image with greater detail and is important for use in a variety of applications. These include medical imaging, robotics, and autonomous driving. However, it is often difficult to accurately segment images, especially in complex scenes with multiple objects and overlapping regions.

3. Image Classification: The task of image classification involves assigning a label or category to an image based on its visual content. It has wide applications in areas such as face recognition, object detection, and analysis of medical images. It is performed using three steps, namely, gathering a dataset of labeled images, extracting features from images, and classifying the images based on their patterns. However, despite its varied applications in the real world, it is a challenging task. This is due to the differences in lighting conditions, image content, and image quality.

4. Image Restoration: The process involves repairing or enhancing images using an image annotation platform where the images have been degraded due to factors such as noise, blur, or compression This is an important task with wide applications in the field of medical imaging, satellite imaging, and digital forensics. This, however, poses a challenge, as it requires the reconstruction of missing or corrupted image data.

5. Video Analysis: Video analysis deals with understanding and analyzing the content of video streams using image annotation services. It has wide applications in the fields of surveillance, autonomous driving, and sports analysis. Since it requires tracking projects over time involving large amounts of data, there are challenges galore. However, with tools such as Zastra, a data labeling platform, the challenges can be addressed.

Conclusion

The application of computer vision technology has picked up across industries, with businesses seeking ways to enhance their ability to capture information from images, detect objects, and gauge their positions using methods such as image annotation. At the same time, the use cases for computer vision are complex and require expertise. So, for businesses to achieve quantifiable ROI, a strong business case for automation should be made. They should make use of data labeling services to enable machines to understand images or videos.