Unique Object Detection and counting of object using ultralytics YOLOV8

Abstract

The "Unique Object Detection and counting of object using ultralytics YOLOV8” represents a solution to time reduction, resource optimization, informed decision, & enhance security resented are systems and methods that identifies a unified object and count the area on the specified area which achieves impressive performance in detecting vertically placed and scattered objects so that user can reduce time in counting these objects. This invention introduces a mobile application, named "Unique Object Detection Counting", designed to revolutionize in counting objects when horizontally placed or scattered. By harnessing cutting-edge technology, this application empowers users to efficiently count their objects within the specified time through Real-Time Image Processing, User-Friendly Interface, Data Repository and Analysis.

Specification

Description:FIELD OF INVENTION
[001] The present invention relates to Information Science & Engineering. More Particularly, the present invention relates to an Image Processing and Machine Learning Algorithm.
BACKGROUND AND PRIOR ART
[002] Object counting on a scattered area is time consuming activity and accuracy in counting objects is tedious task. This invention presents an innovative mobile application that leverages cutting-edge machine learning techniques to predict the accurate count of objects in short time.
[003] The background of the "Unique Object Detection and counting of object using ultralytics YOLOV8" is rooted in the realms of Resource Optimization, Informed Decision-Making, and time optimization. This application emerges in response to several pressing challenges and needs within these fields:
[004] 1. Statistics of certain crops in farmer fields: Crops like coconut and betel leaves are critical crops, counting of these is time consuming activity to farmers in current traditional methods, such as collecting coconut in one place and then counting or visual estimation, can be inaccurate and labor-intensive.
[005] 2. Analytic's of people control during events in auditorium: In a open auditorium helps to obtain count of pupil where controlling future arrangements can be easily made.
[006] 3. Data-Driven Decision-Making: In the modern era, data-driven decision-making is becoming increasingly essential across various domains. In a group gathering, having accurate and real-time data on people,objects enhance the decision-making process. It empowers business analytic's to make informed choices regarding resource utilization.
SUMMARY OF THE INVENTION
[007] The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
[008] In the realm of data management efficient and accurate assessment of objects is essential. This invention introduces a trans-formative mobile application, uniquely harnessing ultralytics YOLOv8 machine learning techniques, to offer real-time calculations of unique objects based on images captured through everyday smartphones or tablets.
[009] The "Unique Object Detection Counting" redefines the landscape of analytic's with its groundbreaking approach. The application's core features encompass: Real-Time Image Processing: Empowering users to capture object images on mobile devices, the application deploys YOLO V8a machine learning to identify objects species and analyze count the objects trained in the image, It can calculate multiple trained objects at a stretch.
BRIEF DESCRIPTIONS OF DRAWINGS:
[010] Figures briefs about Input the image that required the processing of object. Identify object to be processed {Train the object that required for counting}. Point the objects that required to be counted Identify the pointed object that is partially seen if possible, by Grey scale. Count the pointed objects Output the count of object which are seen and picture of the object that are pointed. So that user can identify which are object counted
DETAILED DESCRIPTION OF THE INVENTION
[011] The following description is of exemplary embodiment's only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiment's of the invention. Various changes to the described embodiment's may be made in the function and arrangement of the elements described without departing from the scope of the invention.
[012] The present invention relates to a kind of by understanding objects and object placement conditions, set up the fore-casting model with identifying the object in different conditions and counting of these objects, thus try to achieve accuracy ; by using latest YOLOV8 Digital Image processing and ML analytic's where increase accuracy of predication.
[013] The information such as the specific of the object, specific conditions of object can be obtained by using YOLOV8 (You Only Look Once) algorithm help to count the object which are in the image. For eg: Mobile camera lens along with integration google lens app will help to identify the object. Google internal uses Digital Processing AI and ML to identify the species (YOLO algorithms). This information is read and obtain to object count. , C , Claims:Claim 1: A mobile application for identifying and estimating the number of objects, comprising a real-time image processing module, identifying the object, pointing the object, identifying partial objects, and counting of objects utilizing machine learning techniques, and a user-friendly interface.
Claim 2: The mobile application of Claim 1, wherein the real-time image processing module captures and analyzes object images using YOLO V8a machine learning, pointing the objects and identifying partial of pointing objects.
Claim 3: The mobile application of Claim 1, further comprising a object trained module, wherein captured image data is stored for future analysis, providing insights into different objects that identified other pointed objects.
Claim 4: The mobile application of Claim 1, wherein the pointing employs machine learning feedback mechanisms to continuously improve the accuracy of counting the exact objects and partial of pointing objects.
Claim 5: The mobile application of Claim 1, which estimates count of pointed object based on machine learning algorithms that physical characteristics, thereby supporting resource optimization.

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