Object Detection Technology
Object detection technology gives machines the ability to see. With object detection capabilities, machines and the associated software can identify and locate various types of objects in an image or video. As soon as an object is detected by the software, it is demarcated by a bounding box, imparting the ability to track the location of the detected object.
Axonator uses object detection technology in many of its applications, in the form of inventory management, asset management, and facial recognition system. The object detection technology is also used by us in applications related to tracking of field-workers, applications related to GIS, and all systems and software related to such kinds of applications.
Object detection works on the logic of assigning objects to a particular set of classes. The greater the detail of the classification of objects, the better and more accurate is the output of the object detection software. For example, for detecting circular objects, the software looks for objects at a particular distance from a fixed point (i.e. the center). Similarly, for detecting square objects, the software looks for objects with perpendicular edges and having equal side lengths.
Once the class of each object is defined, the object detection software can detect the defined objects based on the classification system adopted and also determine the location and scale of all object instances. The system is designed in a manner so that objects are detected regardless of the scale of the object, its location, position and pose wrt to the camera, kind of lighting available for analyzing the image, which is used as an input criterion.
Refining this logic further, greater detail of the detected objects can be known using the object detection technology. For example, if an object (a face) is detected by the software, it is also capable of tracking the location of the face, identifying the gender of the detected face, give location intelligence in the form of other objects present in the surroundings, and the description of the scene involved (eg: if the detected face is indoors or outdoors) and various other kinds of contextual information.
Types of Object Detection
- Machine Learning (ML) based approach, where the software analyzes the detected object in terms of color histogram, pixel identification approach, and others and the subsequent feeding of such input data into a regression model, predicting the location of the object along with its other attributes.
- Deep learning (DL) based approach, which uses convolutional neural networks (CNNs) to detect and identify objects, involving advanced algorithms where the extraction and features of the detected object don’t need to be defined separately, as in the case of the ML-based approach.
How it Works?
The DL approach of object detection has two main components, an encoder, and a decoder. An encoder analyzes the image input based on the extraction of statistical features involving blocks and layers and this is used to detect and locate the objects.
The output from the encoder is passed to a decoder, which performs the task of encapsulation of the objects by labeling them as per the object classification. The decoder uses a regression model to predict the location and size of the object, and the greater the detail of the detected objects required, the more the number of parameters is to be determined by the regression model employed for the analysis.
A better and a more refined approach than the regression model is the region proposal network, where the object detection software proposes the regions of an image where it believes the object may be located. The pixels of the selected region in the image are then run through a classification network which in turn detects the object and the associated details. This type of approach is more accurate but demands a lot of computational capacity.
Other methods, like using single-shot detectors (SSDs), where decoders use the non-maximum suppression technique for object detection.
Popular Applications of Object Detection Technology
- Autonomous Vehicles: Better known as self-driving cars, such systems use objection detection technology in real-time to make decisions based on which vehicle is driven. The object detection technology in such systems are capable of detecting humans, say crossing a road, and based on the detected object, the vehicle can be stopped by the program at a safe distance to avoid a collision.
- Face Detection and Pose Detection: Such object detection systems are capable of automatically recognizing a face in an image or in a video and are able to detect faces in a dynamic and unstable environment. Post detection has applications in areas like robotics and detecting human interactions.
- Tracking: An object detected by the object detection technology can be tracked, and the location of the detected object, various details associated with the object, and the kind of environment it is present it can also be identified and such information is useful in applications related to object tracking and surveillance.
Axonator Apps Using Object Detection Technology
Based on the popular applications discussed, the Axonator no-code platform uses the object detection technology in a series of apps developed to give powerful capabilities and value-added features to our clients, resulting in the implementation of better, efficient, and structured business processes leading to enhanced profitability of our client businesses.
Disney Asset Management Solution
The Disney asset management solution is a landmark event for us, for it was developed by our founder and CEO, even before Axonator came into existence. On a visit to the US, Jayesh was approached by the Disneyland management team for developing a solution that would help them in maintaining and tracking a large number of assets, that were spread across their vast campus, spread over X acres.
Based on the object detection technology, Jayesh developed a system for Disney capable of detecting the assets spread across their vast campus, track their location, the condition of the assets, and relay the detected information to their asset inspection and maintenance team so that tasks like asset management, asset tracking, and maintenance and management of the assets could be simplified, using a mobile application. The asset inspectors could also get a list of the nearby assets based on their location on the Disney campus.
Recognizing the potential of the mobile apps to replace the outdated and inefficient pen and the paper-based manual system being used at that time, Jayesh founded Axonator, and presently Axonator is playing a huge part in helping various companies, of all sizes, to leverage digital transformation based on the use of no-code micro-apps.
Quality Management App
The Axonator quality management app uses the object detection technology for inspecting the objects on the assembly line or the manufacturing floor. The objects detected are subjected to automated quality control checks and detect defects in the end-products as well.
Inventory Management App
Automated scanning of the products in a warehouse using the object detection technology, based on which critical information for managing inventory like real-time info of stock levels, classification of stock and the breakup of their stock levels in the warehouse, and automated reading of the QR-codes associated with each product to get detailed information about the product, like the manufacturing date, location history, and others.
Axonator uses object detection technology in its apps related to GIS-based applications where the shape, size, location attributes, and other details of various objects present in a map/image are mapped and detailed with greater accuracy.
QR-Code Based Checklist Display
Devices or the QR code is identifiable through our apps and this QR code is scanned and the corresponding checklist offering info associated with the service users are asking for is displayed on their smartphone.
Future Applications Being Considered By Axonator
- Human-computer interaction (HCI)
- Security systems based on facial recognition and tracking
- Information retrieval systems