How to Improve Customer Experience with Object Detection Software?
You must have heard about object detection before? It is a powerful technique to detect and identify objects on the images. The object detection technology represents an excellent opportunity for enterprise businesses in terms of delivering better experience and services to customers today.
The object detection market will grow up to $38.9 billion by 2021. Object detection is a part of Artificial Intelligence-driven Computer Vision (CV) technology that is used in various industries today- from retail, logistics to industrial sectors and more. It helps businesses to accurately detect, classify and recognize objects from image or video streaming feeds.
To get an in-depth idea about how deep learning-based object detection software assists businesses in delivering a better experience. Read on:
What is Object Detection Software?
The process of object detection involves identifying an object’s location in an image. Object detection is a subset of larger computer vision technology. Computer vision is a field of computer science where emerging technologies like Artificial Intelligence, Machine Learning, etc. are used to train machines to identify and interpret objects from images or videos in a human-like manner.
Now, if we discuss object detection software, the systems of 2010 and early 2010s relied on machine vision, a fundamental counterpart to human vision, to track moving or still objects from images. DPMs (Deformable part based machines) is a popular example.
However, these systems were not effective. This earlier software can detect objects from simple contexts only. For complex scenarios with multiple objects, classes and orientations, these systems failed due to fixed learning capacity, low detection accuracy and other reasons.
This was the primary reason for the rise of deep-learning object detection models.
Deep Learning-Based Object Detection
Image classification and object detection improved drastically with deep convolutional neural networks (DCNNs) after 2010. Still, the lack of powerful computing resources and quality training data affected overall improvement in object detection software.
After some time as quality training data and powerful computing resources became available, applying deep learning models in the object detection pipeline improved the performance of detection software significantly.
Research-led innovation in object detector models and faster adoption by the development community is the reason why most global enterprise software companies today use deep learning technology for building powerful computer vision solutions.
Object Detection vs Object Recognition: Differences That Matters
These two terms are thrown interchangeably by many industry experts. But, there is a significant difference between both that you must know.
Object detection refers to tracing the location of an object in a particular image. Whereas, object recognition identifies objects as a component of a particular class.
Here is an example. An object detection software detects the presence of cloth in an image. But, the recognition software will tell you whether this cloth is a male or female costume.
The important thing is both these processes are not as simple as they seem. Object detection relies on Machine learning and Deep learning models. For a computer, it goes down into breaking a photo into short pixels and then identifying certain patterns or shapes to identify an exact object. The amount and “quality” of data given to an ML model are crucial in image classification. The more data you provide, the faster your ML model will learn from it.
Why Object Detection Software is Essential for Enterprises?
There are two main reasons why object detection software usage is a must for modern enterprises: drive customer engagement and deliver a connected experience.
Object detection is also getting widespread popularity in multiple industries due to its various use cases - ranging from improving productivity to high security at the workplace. Many businesses have used this tech to set up a profitable business venture in the past.
Airbnb is a popular example. It’s important for the home rental service provider to list correct amenities in its Airbnb app to deliver a better customer experience. As it has millions of listings that cover living space on its app, the quality of these listings is important. Airbnb makes sure the advertised ones are close to real ones.
By applying object detection, the goal of Airbnb data scientists is to check whether the amenities or aesthetics shown in the advertised pictures will provide convenience to their guests or not. For instance, customers on a family trip might require an extra spacious kitchen compared to a bachelor.
Finally, if we talk about the use cases of object detection for enterprise software companies, there are plenty. Some of the prominent ones include facial recognition, security & surveillance, object recognition, code recognition, medical image analysis, automated driving, gesture recognition and various others.
Applications of Object Detection Software in Different Industries
Deep learning-based object detection solutions are transforming a variety of industries. Here are some examples:
Retail & Ecommerce
Retail and ecommerce solution providers are leveraging the object detection technology the most to build solutions that improve customer experience.
The most useful application of object recognition software in ecommerce is the image search facility. Alongside voice search, uploading product images to get similar recommendations are the hottest trend in the ecommerce segment.
Another area where object detection works great for retail & eCommerce players is AR /VR based virtual shopping. The combination of image recognition and AR /VR works best to deliver an unmatched shopping experience to customers in retail.
Here are some examples:
The Swedish furniture manufacturer, IKEA allows its customers to check how their product will look in their home by visualizing it via mobile app.
Sephora, the cosmetic company, allows customers to virtually try beauty products in its mobile app before purchase.
Pinterest Lens feature allows users to upload images from their smartphones to get related product suggestions.
Media & Entertainment
Various sports companies use deep learning-based object detection models like Faster RCNN and Yolvo3 to increase the pace and efficiency of the game. In the sports betting and media industry too, computer vision tech is used to obtain in-depth sports information.
Here are some popular examples:
The National Football League (NFL) uses Amazon Rekognition along with Artificial Intelligence, ML and deep learning object detector models to quickly search and store thousands of its media assets like player images.
NFL also uses a deep learning-based object detection model for real-time tracking of football during a match. This is helpful in complex scenarios when the ball is surrounded by a lot of players and is partially visible from multiple camera angles.
Sportradar is investing in computer vision research to build object detector-based data collection capabilities for games like soccer, tennis and snooker.
The healthcare solution providers are putting their bet on deep learning-driven object detection tools to detect symptoms of various diseases at an early stage. By scanning X-rays, CT scans and MRI reports of patients, disease identification and prevention at an early stage is possible.
Here is an example
Neural network-based object detection tools are used for cell nuclei counting and identification by medical experts.
Implementation of object recognition technology in the automotive industry in producing breakthrough results for businesses. One area that is benefitting the most is the development of driverless vehicles.
Object detection tools allow self-driving cars to identify traffic signs, people, pedestrians, road markings and other vehicles with minimum human involvement.
Companies like Tesla, Waymo, Tata and Uber are approaching autonomous vehicles by effective use of object recognition technology.
Volvo, the Swedish automotive giant, is using object recognition technology to predict which parts of its car will break down early to improve the service quality.
Shipping & Logistics
Shipping and logistics solution providers can use object detector solutions to automate various business processes. It includes inventory planning, monitoring, checking stocks, quality control, and various other processes. Such an approach will help businesses improve their worker’s productivity level, streamline supply chain and improve overall customer service.
Here are a few examples:
Qopius is using AI-based computer vision and object detection technology to detect and recognize the item’s brand, logo and price. This can help companies in tracking the shelf’s performance of their products, including items out of stock, inventory space availability and more.
Zenrobotics has built an AI-based robotic arm solution for intelligent waste sorting to optimize its supply chain.
Object Detection Technology - An Enabler For Customer Success
In a nutshell, object detection is among the top
technologies that can model your business development while setting its overall
direction. If operationalized properly, a business can experience higher
throughput, boosted productivity and better customer experience with
object detection technology solutions.
Businesses should analyze whether the implementation benefit of object detection tech outweigh its expenses or not. Setting a clear development strategy is vital also to get the desired business results.
As an experienced IT consulting and technology solution provider, A3logics deliver customized solutions based on advanced AI and machine learning algorithms, If you are planning to build deep learning solutions for your business, we can help! Our solutions experts can integrate deep learning algorithms in your object detection software to drive utmost customer satisfaction for your business.
Drop us a line to know more about our technology services.
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