AI Image Reliability Inspection for Vehicle Detection
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Solution Overview
Problem
Current vehicle detection algorithms face limitations due to mislabeled or inaccurate images in training sets, and the computational intensity of machine-learning methods makes them unsuitable for handheld devices, which also struggle with non-uniform and varied image quality from mobile devices, affecting accuracy and user experience in real-time applications like augmented reality.
Innovation Solution
A machine-learning artificial intelligence system that categorizes images, calculates probability outcomes, determines image reliability, and filters out low-reliability images by using a processor in communication with a client device to obtain and analyze images, classify them, calculate probabilities, and rank their reliability, thereby improving the accuracy of vehicle identification and user experience on handheld devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning algorithms are used for vehicle identification, then identification accuracy is improved, but computational intensity increases making them unsuitable for handheld devices
Solution Approach 1:
The system segments the image analysis task into two parts: a server-based machine-learning algorithm for accurate classification, and a handheld device-based reliability inspection using simpler algorithms. This divides the computational workload, allowing accurate identification on servers while keeping handheld devices lightweight enough for portable use.
Solution Approach 2:
The patent introduces an intermediary server system that handles the computationally intensive machine-learning vehicle identification, while handheld devices only perform simpler reliability inspection tasks. This intermediary architecture allows accurate identification without requiring high computational power on the handheld devices themselves.
2Measurement precision
If machine-learning methods are used for image analysis, then identification precision is improved, but the methods become difficult to perform in handheld devices
Solution Approach 1:
The system segments functionality by performing complex machine-learning identification on remote servers while handheld devices only execute simpler reliability inspection algorithms. This maintains high identification precision through server-based ML while preserving ease of operation and portability on handheld devices.
Solution Approach 2:
The server acts as an intermediary that provides the sophisticated machine-learning identification capabilities, allowing handheld devices to access high-precision identification without needing to run the computationally intensive algorithms themselves, thus maintaining both precision and portability.
3Speed
If images are analyzed quickly for user-friendly portable applications, then user experience is improved, but accuracy rate may be compromised due to limited processing time
Solution Approach 1:
The system segments the analysis process into two stages: rapid reliability inspection performed locally on handheld devices using simple algorithms, followed by comprehensive machine-learning analysis on servers. This allows quick initial assessment and user-friendly speed while maintaining high accuracy through subsequent server-based processing.
Solution Approach 2:
The handheld device performs preliminary reliability inspection immediately upon image capture using fast, simple algorithms. This preliminary action provides quick feedback and filters out obviously unreliable images, enabling user-friendly speed while the server performs more thorough analysis only on images that pass the preliminary screen, thus maintaining accuracy without sacrificing speed.
4Adaptability or versatility
If training algorithms use diverse image sets including low-quality mobile images, then adaptability to real-world conditions is improved, but mislabeled or inaccurate images reduce overall detection accuracy
Solution Approach 1:
The system performs preliminary reliability inspection on mobile device images using simple algorithms before including them in training data. This preliminary action filters out mislabeled or inaccurate images, ensuring that only reliable images proceed to the machine-learning training process. This maintains adaptability to real-world mobile imaging conditions while preventing inaccurate images from degrading detection accuracy.
Solution Approach 2:
The reliability inspection system provides feedback by evaluating image quality and labeling accuracy before images enter the training set. This feedback mechanism ensures that only images passing the reliability threshold are used for training, thereby maintaining high detection accuracy while still adapting to the diversity of real-world mobile imaging conditions.
Data Source
AI summary
A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.