Recorded Image Alignment for Accurate Telecom Equipment Counting
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Solution Overview
Problem
Current methods for accurately counting telecom equipment in vehicle survey images are labor-intensive, time-consuming, and often inaccurate due to the reliance on manual counting or object tracking techniques that are ineffective for non-consecutive high-resolution images.
Innovation Solution
An AI-based solution that aligns and compares location and visual similarities across non-consecutive images using machine learning models to identify and count telecom equipment, such as antennas and RRUs, by aligning images based on structure and object features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting by humans is used, then counting accuracy can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical counting with an automated computer vision system that uses image processing algorithms to detect and count telecom equipment. The system processes images automatically through algorithms that identify objects, track them across frames, and maintain counts without human intervention, thereby eliminating the trade-off between accuracy and efficiency.
Solution Approach 2:
The system performs self-service by automatically processing images and maintaining its own counting state without external human intervention. The computer vision system independently detects objects, tracks them across multiple images, and maintains accurate counts through automated algorithms, making the counting process self-sufficient and efficient.
2Extent of automation
If object tracking algorithms are used for video feeds, then automation is achieved, but the method becomes ineffective for non-consecutive high-resolution images
Solution Approach 1:
The patent changes the fundamental parameters of image processing by switching from frame-based temporal tracking to feature-based spatial matching. Instead of relying on temporal continuity of video frames, the system uses distinctive visual features and their spatial relationships to identify and track objects across non-consecutive images, making the tracking reliable for sparse image sequences.
Solution Approach 2:
The system segments the counting task into independent object detection, feature extraction, and matching components. By detecting distinctive features in each image and matching them across the image set, the system can accurately track objects even when images are non-consecutive, maintaining reliability through segmented processing rather than continuous video analysis.
3Quantity of substance
If manual inspection and climbing towers is used, then comprehensive equipment inventory can be obtained, but time consumption and safety risks increase
Solution Approach 1:
The patent uses visual copying by capturing images of telecom equipment from remote locations and processing these image copies to create an accurate equipment inventory. Instead of physically inspecting each piece of equipment on-site, the system analyzes visual representations (images) to identify, locate, and count all equipment, thereby obtaining complete inventory data without time-consuming physical inspections.
Solution Approach 2:
The system introduces image processing algorithms as an intermediary between remote imaging and equipment inventory creation. This intermediary automatically processes images to detect and identify equipment, translating visual information into structured inventory data without requiring human inspectors to physically access or manually record equipment details.
4Productivity
If conventional image processing is used for telecom equipment counting, then processing speed is maintained, but counting accuracy deteriorates due to inability to distinguish similar objects
Solution Approach 1:
The patent applies local quality analysis by examining distinctive visual features at specific locations within images. Instead of treating all regions uniformly, the system focuses on identifying and analyzing unique local characteristics of objects (such as antenna structures, equipment shapes, and their spatial arrangements) to accurately distinguish between similar objects while maintaining processing efficiency through targeted feature analysis.
Data Source
AI summary
Embodiments herein disclose a method performed by a computing device (11) for handling an object in one or more recorded images of a structure. The computing device (11) receives from one or more mobile devices (15), at least two recorded images of the structure. The computing device (11) aligns the at least two recorded images vertically and/or horizontally based on the structure and/or an object in the at least two recorded images. The computing device (11) further computes a location similarity of the object in the at least two recorded images by comparing location values of the object in the aligned at least two recorded images, and computes a visual similarity of the object in the at least two recorded images by comparing visual characteristics of the object. The computing device (11) further determines whether the object is the same or not in the at least two recorded images.