Object Detection In Real-Time Data Processing

Can you imagine a tool that can analyze a custom object in real-time? Implementing computer vision artificial intelligence tools empowers a machine to learn and understand a specific object from the provided video or image using object detection algorithms. In this blog, we will talk about two real-time applications of computer vision technology that use deep-learning models to identify different objects. Let’s see how we have implemented artificial intelligence programs to develop a multi-functional object detection tool.

How Does Computer Vision Use Object Detection?

Computer vision technology has reorganized and simplified the process of image recognition by utilizing artificial intelligence and object detection techniques. Here’s a demo of our AI-based tool using object detection models and other web development languages to detect and analyze a specific object from the input image or video. For example, this tool provides a constructive platform to address common traffic-related challenges, such as speed detection, road signal vulnerabilities, vehicle congestion, and other pedestrian security concerns. Therefore, it automates the monitoring process and accurately identifies objects to optimize the traffic insights.

Furthermore, you can use this tool for inventory management. With this object identification system, businesses can systematically manage the ordering process, unit storage, inventory tracking, and other analyses. So, what are the key techniques or methods of computer vision used in object identification and image recognition?

Neural Network Impact On Object Detection

The core functioning of automatic traffic surveillance lies in the neural network, specifically CNN (Convolutional Neural Network). It processes sensor data by redefining features like edges that are termed classification to sort objects into categories effortlessly. It allows you to navigate the surroundings before moving or parking your vehicle anywhere. CNN has multiple layers and frameworks to initiate object detection to extract image data from sensors. Every single network layer contributes in decoding the image file to create a clear visualisation. It differentiates a particular object from another through the integration of convolution, pooling, and connections. Object detection models use a few important traits for identification purposes.

• Extraction

Spotting an object is the key element where the convolution layer shows its impact by fine-tuning images to get clarity. Through preprocessing workflow, you can resize images, perform data augmentation, and normalize pixel values. It extracts vital cues, such as edges, colours, shapes, and other objects.

• Classification

After completing the identification process, it focuses on matching the captured objects with their assigned labels. That means the concentrated models predict bounding boxes and sync the object to a specific class. For this feature, autonomous vehicles quickly make decisions to react according to the significance and damage risk of the object. Another example of a computer vision-based application is a self-driving vehicle. It uses adaptive detection skillfully to avoid obstacles, check traffic signs, and differentiate other vehicles on the road.

Object Detection Using Machine Learning

The influence of machine learning in object recognition is remarkable compared to traditional methods of detection. It can train an object-detecting machine like an AI security camera, CCTV camera, or other devices to recognize, analyze, and localize an object in videos and images. We have developed an object detection system that performs tasks based on your instructions.

For instance, you can utilize this to access data through a CCTV camera, like scanning a QR code, delivery orders, product availability, and other objects. The role of machine learning algorithms depends mostly on deep learning models that perform with better accuracy and speed. For example, Python libraries like OpenCV, PyTorch, and TensorFlow are useful in extracting and decoding image patterns for further data analysis. So, here are a few deep-learning models that are widely used in modern object detection processes:

• R-CNN (Region-Based Convolution Neural Network) For Image Classification

It is a high-end ML model that scans and selectively searches to get the result in an SVM (Support Vector Machine). It detects and classifies the object based on the shape and edge that exists in the scanned images. Fast R-CNN is the updated model that enables end-to-end object detection similar to R-CNN.

• YOLO (You Only Live Once) For Accurate Real-Time Detection

You must have heard the term YOLO (You Only Live Once) in the process of object detection. It is one of the essential algorithms that use deep learning techniques to predict object locations and classes in one go. Contrary to R-CNN, YOLO has a different approach that segregates the captured image through grid lines and anticipates the bounding boxes. Therefore, it is easier to figure out the object’s class probabilities as per the grid cells. Thus, AI development companies use the YOLO model in real-time object detection applications.

• SSD (Single-Shot Detection) For Single-Pass Prediction Mechanism

Single-shot detection is another single-stage detector like the YOLO that uses deep-learning techniques for object detection. It works efficiently for predicting and determining classes and objects. However, SSD utilizes a deep neural network to identify object bounding boxes in addition to corresponding class probabilities. For instance, it gives the best results in capturing fast detection like surveillance systems, self-driven cars, and facial detection.

Here’s the structural flow that may give you a better idea of SSD. Recorded images go through multiple convolutional layers that consist of various features mapped according to different scales. Through convolution filters, SSD evaluates the object’s exact location in the presented image to create maps and bounding boxes. Similar to Faster R-CNN, every single bounding box engages in predicting the object’s class probabilities. Lastly, the end result comes out from the best-predicted box output based on the IoU (Intersection Over Union) metric in computer vision.

Applications Of Object Recognition Using Deep Learning

Several approaches and AI-based tools are available to enhance the image recognition process by determining necessary objects. Here are a few use cases of our advanced object detection application utilizing deep-learning algorithms to improve traffic policing and inventory management.

How Object Detection Transforms Traffic Control Management?

• Traffic Congestion Monitoring- Primarily, the vehicle detection method uses two technologies: image processing methods and CNNs. By implementing modern approaches based on CNNs, it has become easier to track objects with better precision in real-time. YOLO enables you to capture motion attributes and give accurate results from the source image after data processing. For instance, this AI-based application helps in tracing vehicles violating traffic rules. Specifically, traffic regulators use IP cameras and object detection algorithms to monitor real-time incidents, count vehicles, adjust traffic signals according to live data, and other incidents.

• Pedestrian Safety- Pedestrian detection in self-driven vehicles and electric cars helps you determine the positioning of an individual on the road. YOLO or SSD algorithm analyzes video footage in real-time from traffic signal cameras to detect pedestrian movements, running vehicles, traffic irregularities, and other movable objects seamlessly. Another use case of an object detection system using deep learning algorithms is the automation of traffic signals at pedestrian crossings.

• Helmet & Number Plate Detection- Traffic regulators can track motor vehicles or bikes with unverified number plates. It is also fruitful for toll plazas to automate the verification of a vehicle (insurance, owner’s name, authorization, pollution, and other documents). For instance, Capsquery has designed an object detection system in such a way that can identify helmets. If an individual is not wearing a helmet on the road, traffic regulators can easily monitor that person from the number plate and take the necessary actions.  

Benefits Of Using Object Detection Algorithms In Inventory Management

Another example of a computer vision technique is object detection in inventory management. Our AI-based application helps you to automate inventory checks in real-time without the intervention of humans. Therefore, it minimizes labour costs, saves time & effort, and streamlines inventory management with fewer errors. Additionally, retail shops can use object detection through IP cameras or CCTV cams to check customer behaviour, stock placement on shelves, and prevent theft. A few key functionalities you can achieve through this computer vision application in inventory management:

• Stock Unit Monitoring- Integrating object detection functionalities with your IP security camera can track every product available in the inventory. It gives you accurate information in real-time according to the stock levels.

• Product Placement- With this application, you can identify the specific positioning of the product. For instance, if there’s a mismatch in product units or items are wrongly placed, this app makes quick data analysis and figures out the errors. This way, you can notify a warehouse worker to reposition the items in the correct place.

• Damage Detection- It is highly essential to update stock and check damaged units. With object detection, it is convenient to trace mislabeled and damaged products in the stock.

Factors You Should Consider To Use Object Detection Model

Object detection facilitates streamlining several operations based on your business needs. Before using this high-end system, a few things you should consider:

• Production Cost & Maintenance- To develop an AI-based object detection system requires proper budget planning. The development process involves several key elements like cloud hosting space, database management, hardware components, and a reliable development team. Based on your business model, you need to implement this high-end application for object detection automation.

• Real-Time Data Analysis- If your purpose is to detect objects through live monitoring of inventory, logistics, traffic surveillance, car parking, automated checkouts, and other operations, then faster computer vision algorithms like YOLO, CNN, and SSD are effective.

• Training Model Capabilities- It is important to find out whether you have sufficient technological resources to customize or train an object detection model to meet your business needs. If you need further information regarding custom training to determine an object, then talk to our AI app developers.

Bottomline

The objective of implementing an object detection system is to automate the identification process. It mainly consists of object classification (determining types of objects in an image) and localization (identifying detected objects’ positions through bounding boxes). Using these models in real-time data analysis applications simplifies the identification process from videos and images with better accuracy. Talk to our AI developers for a quick demo request of our custom object detection system that provides real-time data. Drop your comments to learn more about this evolving technology.