Composite Target Object Imaging With Chain-of-Custody Integrity
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Solution Overview
Problem
Construction sites face challenges in identifying people, equipment, and hazardous conditions in an evidentiary manner, preserving image chain of custody, and maintaining image quality for worker safety and regulatory compliance.
Innovation Solution
A method to identify, acquire, and accrete a target object image from a Target Object Waif Artifact within an unqualified-unreserved image, using a deep learning/machine learning environment to create a complete image, with a Docu-Narrative system for image maintenance and authentication, ensuring evidentiary quality and chain of custody.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image capture methods are used at construction sites, then image acquisition is simple, but image completeness and quality are insufficient for evidentiary purposes
Solution Approach 1:
The system performs preliminary actions by capturing multiple images from different angles and distances before needing to assemble the final composite image. This ensures that all necessary visual information is already available, eliminating the need for complex post-capture navigation and reducing the complexity of real-time image processing.
Solution Approach 2:
The image processing system is segmented into distinct functional modules: image capture, image processing (including enhancement and stitching), and chain of custody management. This modular segmentation allows each component to be optimized independently, improving overall image quality while keeping the system manageable and not overly complex.
2Loss of information
If multiple images are captured and processed to form complete images, then image completeness improves, but processing time increases
Solution Approach 1:
The system performs preliminary image processing operations such as enhancement, stitching, and composite image creation immediately after image capture, while the data is still in memory. This preliminary action ensures complete information is preserved and processed efficiently before storage, reducing delays later in the workflow.
Solution Approach 2:
The image processing operations continue seamlessly from capture through enhancement, stitching, and composite image creation without interruption. This continuous processing workflow eliminates idle time between operations and ensures that image completeness is achieved without unnecessary delays.
3Reliability
If image chain of custody is maintained through multiple processing steps, then evidentiary reliability improves, but system complexity increases
Solution Approach 1:
The image management system is designed with multi-functionality, handling image capture, processing, enhancement, stitching, composite image creation, and chain of custody tracking within a single integrated platform. This universality improves evidentiary reliability by ensuring all operations occur within one controlled environment, while avoiding the complexity of coordinating multiple separate systems.
Solution Approach 2:
The system implements feedback mechanisms that track and record each processing step, enhancement applied, and modification made to every image. This automatic feedback loop maintains comprehensive chain of custody information without requiring manual documentation, improving reliability while keeping the system automated and not overly complex.
4Measurement precision
If deep learning algorithms are used for image processing, then image accuracy and completeness improve, but computational requirements increase
Solution Approach 1:
Deep learning algorithms are applied in advance during the image processing stage to identify objects, hazards, and equipment before the images need to be analyzed for evidentiary purposes. This preliminary application of computational energy ensures high accuracy in object identification while allowing for energy-efficient storage and retrieval of already-processed images later.
Data Source
AI summary
A method for augmenting a target object orphan artifact image within a composite image. First, determining that only a target object orphan artifact image appears in the composite image and that a complete target object can be created. Second, reviewing a plurality of ground truth images and curated images of the target object and incorporating image elements therefrom into the target object orphan artifact image to create a target object child image. Third determining whether the target object child image is adequate based on a rubric score. If the rubric score indicates that the target object child image is adequate the method terminates. If the rubric score indicates that the target object child image is not adequate the method returns to the step of selecting another ground truth image or curated image of the target object.