A Brief Overview to Object Detection in Computer Vision

Object detection in computer vision

Object detection in computer vision tasks helps detect objects in digital images including photos or videos. Its aim is to develop computational models which offer the most basic information required by computer vision applications.

It utilizes neural networks for localizing and classifying objects within images.

• Localizing involves the accurate identification and marking of position of objects within an image. It involves utilizing a bounding box for outlining the object’s location.
• Classification involves determining the category or type of objects within the image like dog, cat, tree, etc.

Difference between object detection and image classification

Object detection differs from image classification since both of these form the backbone of several AI applications. They can range from gesture recognition to traffic sign detection. Image classification in computer vision involves an annotator assigning a label to an entire piece of data (image or video). Object detection goes a step further. It recognizes and pinpoints the positions of several objects in an image.

Working of object detection

Object detection in computer vision is a complicated technical process and involves several key steps which are highlighted below.

Image Preprocessing
This first step involves resizing the image to a standard size, adjusting the pixel values, and converting the image into a format which is ideal for a neural network. It aids in standardizing the input making it easy for the network to process.

Feature Extraction
This is the second step and involves analyzing images by the computer for detecting low-level features like edges, corners, and textures. Convolutional Neural Networks (CNNs) are normally used for this. Filters are applied to images by convolutional layers to assist in creating feature maps and capturing key details regarding the image content.

Region Proposal
After feature extraction, the network suggests possible regions where objects may be located. Region Proposal Networks (RPNs) produce bounding boxes around areas of interest. A detailed analysis is then carried out by feeding these regions into further layers.

Classification and Localization
Classification involves identification of objects within a region whereas localization involves fine-tuning of bounding box coordinates that enclose the object. This is generally obtained via layers that output class scores and bounding box offsets.

Non-Maximum Suppression
Non-maximum suppression (NMS) is applied to get rid of non-functional and overlapping bounding boxes. NMS makes sure that only the most confident detections are retained by limiting false positives, hence enhancing the accuracy of detected objects.

Bounding Box Regression
This step involves the network adjusting positions of bounding boxes so that it fits the detected objects accurately. It involves the regression of coordinates of the bounding boxes as per predicted offsets from the network.

Post-processing
This last step ensures accuracy and reliability of detections. It includes techniques like thresholding which involves discarding of detections with low confidence scores. Further refinements are carried out to ensure the bounding boxes are in alignment with the objects.

Techniques used in object detection

Object detection can be done using two techniques; traditional and deep learning.

Traditional method
The Traditional method involves manual feature extraction and machine learning algorithms like Viola-Jones Detector, Histogram of Oriented Gradients (HOG) Detector, and Deformable Part-based Model (DPM).

• Viola-Jones Detector: This involves manually defining basic rectangular features that capture variations in intensity. The feature selection is done based on its ability to differentiate object and non-object regions.
• Histogram of Oriented Gradients (HOG) Detector: This is computed inside local cells to capture edge orientations in an image. They are crafted manually for capturing the way edges appear as well as contours in varying orientations.
• Deformable Part-based Model (DPM): This involves the manual definition of the appearance and geometric models of each part. The model’s capability to take into account deformations and spatial relationships is also crafted as per prior knowledge.

The deep learning method
The key technology in deep learning for object detection is Convolutional Neural Networks (CNNs). This involves the automatic extraction of key features from images and learning complex patterns which represent various object categories.This method involves two types of object detection architectures which are based on CNN.

• One-Stage Detector: These are simple and efficient as they don’t require a separate proposal generation step. They are used to directly predict bounding box coordinates and class probabilities for several objects in a single pass via the network. They are faster and can be detected in a single pass.
• Two-Stage Detectors: These are precise, however have a tendency to be much slower owing to the extra proposal generation step. This has two key steps; region proposal and refinement. These steps allow for precise localization and classification. These tend to be slower owing to the extra region proposal step.

Applications of object detection

Object detection has many applications across sectors. It lends computers the capability to see like humans. This assists with automating manual tasks and creating AI-based products and services. Let’s now look at the key applications of object detection in computer vision along with examples to gain a better understanding of its impact.

Retail
People counting systems placed at strategic locations across various retail stores collect information about customer’s behavior. It not only counts people, but measures footfalls. Customer analysis done using AI detects and tracks customers through cameras. This assists in gaining knowledge regarding customer interaction, customer experience, store layout optimization, and enhancing store operations.

Autonomous Driving
Self-driving cars or autonomous vehicles rely on object detection to recognize pedestrians, traffic signages, vehicles, and much more. Examples of self-driving cars are Tesla’s Autopilot and Waymo.

Agriculture
Object detection in agriculture aids with the counting and monitoring of animals. It also assists with the evaluation of the quality of agricultural products. Hence, through machine learning algorithms damaged produce can be detected during processing.

Security & Surveillance
Object detection is used in security for detecting people. A vast range of security applications in video surveillance depend on object detection to detect people in danger zones or access restricted areas. It helps in preventing suicides by raising timely alerts, automation of inspection tasks in remote locations via computer vision.

Transportation
AI is used for detecting and counting vehicles for traffic analysis. It is also used for detecting cars stopping in dangerous spots like highways or crossroads.

Healthcare
Object detection in the medical community has enabled several breakthroughs. Medical diagnostics rely largely on studying images, scans, and photographs. Object detection involving CT and MRI scans has played a key role in diagnosing diseases, for instance, machine learning algorithms for detecting tumors.

Future of object detection

Object detection in computer vision is undoubtedly a key technology and plays an integral role in the development of efficient, accurate and adaptable AI models. It involves exploration of new architecture, enhancing the interpretability of models, as well as limiting the AI model’s dependency on a vast range of labeled datasets.

The scope and impact of object detection systems can be enhanced by integrating it with other key technologies like augmented reality and edge computing. The reliance on labeled training data can be reduced in a big way with advancements in unsupervised and semi-supervised learning methodologies.This helps in tackling a major challenge when it comes to training object detection models. Also, research in ethical AI and bias mitigation is critical to ensure object detection technologies are used in a fair and responsible manner.

Summing Up

The development of AI models which can detect, identify, classify and track various objects requires large quantities of training data. Cogito specializes in preparing and providing training data which lend machines the capability to think, see, observe and understand.

We also offer a combination of manual and AI techniques to our clients along with computer vision training data to suit their AI models. Monitoring and surveillance applications are automated too for sensing, analyzing, and interpreting digital images, videos, etc for making appropriate recommendations or actions.