Identification information content intelligent detection method based on visual ergonomics

Abstract

The invention relates to the technical field of visual information detection, and discloses an identification information content intelligent detection method based on visual ergonomics. The method comprises the following steps: acquiring identification visual data, and obtaining a high-resolution image of a target identification and related environmental parameters; based on the collected data, extracting visual ergonomics characteristic functions including brightness distribution, color contrast and text definition indexes; constructing a self-adaptive detection model framework by utilizing the characteristic functions; and performing iterative optimization on the model framework according to a performance objective, operation limitation and environmental constraint of the detection task: inputting feature representation and outputting a detection result evaluation value, performing content compliance analysis on the evaluation value to generate a deviation metric value, judging whether the deviation metric value reaches a preset stable condition or not, and if so, outputting a final detection conclusion, and if not, adjusting the internal parameters of the model and repeating the process until a preset stable condition is met. The method can realize intelligent detection of the identification information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of visual information detection, in particular to an identification information content intelligent detection method based on visual ergonomics. BACKGROUND

[0002] In modern society, various types of signs are widely used in public spaces, workplaces and daily life scenarios, and they bear the functions of information transmission and guidance. The effectiveness of these signs directly depends on the degree to which they are accurately perceived and understood, and the visual ergonomics characteristics are the key factors affecting the perception effect.

[0003] Traditional identification information detection methods mostly rely on manual inspection, and professional personnel make subjective judgments on the brightness, color, clarity and other aspects of the signs according to their experience. This method is not only time-consuming and labor-intensive, but also affected by individual experience, physiological state and other factors, resulting in poor consistency of detection results. At the same time, manual detection is difficult to cover all signs in complex environments, especially in large public places or dynamic changing scenarios, which is prone to omissions or misjudgments.

[0004] With the development of digital image processing technology, some detection methods based on machine vision have emerged, but they often focus on the detection of a single feature, such as only analyzing color contrast or text clarity, ignoring the relevance between features and the influence of environmental factors. For example, some methods only use fixed thresholds to determine whether the sign brightness meets the requirements, without considering the differences in human visual perception of brightness under different lighting conditions; other methods do not consider factors such as font size and background complexity when analyzing text clarity, resulting in deviations between the detection results and actual visual experience.

[0005] There is a lack of mechanism in the prior art to dynamically adjust the detection strategy according to the detection task requirements and environmental constraints. Signs in different scenarios have different functional requirements, such as traffic signs that need to maintain readability in strong light, weak light and other environments, while indoor signs pay more attention to coordination with the surrounding environment. Fixed detection models are difficult to adapt to these differentiated needs, limiting the applicability of the detection results. SUMMARY

[0006] The purpose of the present application is to provide an identification information content intelligent detection method based on visual ergonomics to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides an identification information content intelligent detection method based on visual ergonomics, which comprises: Collecting identification visual data to obtain high-resolution images of target signs and related environmental parameters; Based on the collected identification visual data, a set of visual ergonomics feature functions are extracted, including brightness distribution, color contrast, and text legibility indicators; An adaptive detection model framework is constructed using the extracted visual ergonomics feature functions; According to the performance target of the detection task, operation limit and environmental constraint, an iterative optimization process is performed on the adaptive detection model framework, specifically including: The feature representation of the identification content is input into the adaptive detection model framework, and the detection result evaluation value is output; The output detection result evaluation value is analyzed for content compliance, and a deviation measure value is generated; It is judged whether the deviation measure value reaches the preset stable condition, if it reaches, the final detection conclusion is output and the process is terminated, if it does not reach, the parameter updating operation is performed; According to the deviation measure value and the optimization mechanism, the internal parameters of the adaptive detection model framework are adjusted, and the above input, analysis, judgment and updating operations are repeated until the preset stable condition is met.

[0008] Preferably, the visual ergonomics feature functions are obtained by processing the block region of the identification image, each feature function is expressed as a multidimensional data array, the array size is directly related to the image pixel density, and the environmental parameters are integrated into the feature calculation during the processing to enhance the data dimension.

[0009] Preferably, the adaptive detection model framework integrates a data preprocessing layer, a feature fusion module and a result generation unit, wherein the feature fusion module is composed of multiple interactive processing units, each interactive processing unit includes a feature encoding component, a weight optimization component and an output synthesis component; the feature encoding component encodes the key information of the input identification visual data using the visual ergonomics feature functions to generate a set of encoded features; the weight optimization component applies a dynamic adjustment mechanism to assign importance to the set of encoded features; the output synthesis component integrates the set of weighted features and transmits them to the result generation unit.

[0010] Preferably, the performance target of the detection task includes one or more combinations of identification readability improvement target, color recognition optimization target, and information transmission rate maximization target, and the performance target is defined by user input or preset standard.

[0011] Preferably, the operation limit includes one or more of the upper limit of processing delay, the threshold of computing resource consumption, and the boundary of storage capacity, and the operation limit is imported through a system configuration interface and affects the training efficiency of the adaptive detection model framework.

[0012] Preferably, the environmental constraints relate to one or more of the range of ambient light intensity, the range of observer viewing angle, the degree of background interference, and the environmental constraint data is sourced from sensor acquisition and interacted with the visual ergonomics feature functions to modify the inputs to the adaptive detection model framework.

[0013] Preferably, the feature representation of the signage content comprises a pixel matrix mapping or a visual ergonomics feature function, and the detection result evaluation comprises a compliance score, a defect label set or an optimization suggestion vector, and bidirectional data transfer is implemented between the feature representation and the detection result evaluation via the adaptive detection model framework.

[0014] Preferably, the content compliance analysis is performed using image processing techniques, including one or more of a feature matching engine, a pattern recognizer or a neural network classifier, and the analysis process receives the detection result evaluation and outputs a deviation metric value for subsequent optimization.

[0015] Preferably, the preset stable condition is defined as triggering termination when the continuous variation amplitude of the deviation metric value maintains below a convergence threshold value, or the number of iterative executions exceeds a preset upper limit, and the convergence threshold value is dynamically adjusted by the performance target.

[0016] Preferably, the optimization mechanism includes one or more of an adaptive learning rate adjustment technique, a swarm intelligence algorithm or a meta-heuristic search strategy, and the optimization mechanism updates the parameters based on the deviation metric value and feeds back to the adaptive detection model framework.

[0017] Compared with the prior art, the present application has the following beneficial effects: By collecting high-resolution images of the target signage and related environmental parameters, comprehensive and detailed raw data is provided for subsequent visual ergonomics feature analysis. High-resolution images can retain the subtle features of the signage, such as text edges and color gradients, while environmental parameters cover external factors that affect visual perception, such as light intensity and surrounding object reflectivity, making feature extraction more realistic for visual scenes.

[0018] Based on the collected data, visual ergonomics feature functions including luminance distribution, color contrast and text legibility indicators are extracted, achieving multi-dimensional characterization of the visual properties of the signage. The luminance distribution feature reflects the luminance variation law of each region of the signage, avoiding the one-sidedness of a single luminance value; the color contrast feature not only considers the color difference between the signage subject and the background, but also combines the perception characteristics of the human visual system for different color combinations; the text legibility indicator takes into account font style, character spacing, background interference and other factors, making it more close to the human visual recognition process of text. The combination of these feature functions can comprehensively capture the key attributes of the signage in visual presentation.

[0019] The adaptive detection model framework constructed integrates the above multi-dimensional feature functions, forming an analysis system that can comprehensively consider the correlation of various factors. The framework does not simply make independent judgments on each feature, but rather relates each feature through internal logic to form an evaluation of the overall visual ergonomics characteristics of the sign. For example, when analyzing text legibility, the brightness distribution characteristics are combined to consider the impact of different brightness regions on text recognition; when evaluating color contrast, the ambient light parameters are associated to analyze the changes in color perception under different lighting conditions.

[0020] The iterative optimization process enables the detection model to dynamically adjust according to the performance targets of the detection task, operational constraints, and environmental constraints. By inputting feature representations and outputting detection result evaluation values, and then generating bias measurement values based on content compliance analysis, the model can continuously perceive the gap between its detection results and actual requirements. When the bias measurement value does not meet the preset stability condition, the model adjusts its internal parameters based on the bias measurement value and the optimization mechanism, and re-performs detection analysis. This cycle of adjustment process enables the model to gradually adapt to different detection scenarios and task requirements, such as enhancing adaptability to strong light and backlight environments in traffic sign detection, and optimizing filtering ability for complex backgrounds in indoor sign detection. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A timing diagram of the intelligent detection method of sign information content based on visual ergonomics according to the present application; Figure 2 A flowchart of the adaptive detection model framework; Figure 3 A flowchart of environmental constraint interaction; Figure 4 A flowchart of bidirectional transfer of feature representations and evaluation values; Figure 5 A flowchart of optimization mechanism parameter update. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] Please refer to Figure 1 The present application provides an intelligent detection method of sign information content based on visual ergonomics, which comprises: The intelligent detection of identification information is realized through multi-dimensional feature extraction and adaptive model optimization. The overall implementation scheme includes the following steps: collecting high-resolution images of the target identification and related environmental parameters to form the original visual data set; after preprocessing the collected data, the visual ergonomics feature function including brightness distribution, color contrast and text clarity indicators is extracted; an adaptive detection model framework including data preprocessing layer, feature fusion module and result generation unit is constructed; based on performance targets, operation restrictions and environmental constraints, the model is iteratively optimized, the detection result evaluation value is obtained through input feature representation, the deviation measurement value is generated through compliance analysis, and whether to terminate the process or continue parameter updating is determined according to the preset stability condition, and finally the detection conclusion is output.

[0024] Embodiment 1: Extraction process of visual ergonomics feature function, which realizes multi-dimensional feature representation by using block region processing technology. In the image data preprocessing stage, geometric correction and noise suppression are first performed to eliminate distortion and interference signals that may be generated in the collection process. After the input high-resolution image is converted to a standard color space, it is divided into several analysis units according to the preset grid division strategy. The size of each analysis unit is dynamically determined according to the total number of image pixels, and the minimum processing unit is not less than the benchmark specification of 10x10 pixels. This design not only preserves the local detail features but also controls the computational complexity.

[0025] In the brightness distribution feature extraction link, pixel-level statistical analysis is performed in each grid unit. The calculation process considers the human eye visual characteristics and uses a nonlinear conversion curve to map the original RGB values to a perceptually uniform brightness space. Each analysis unit outputs three brightness-related indicators: regional average brightness reflects the overall light and dark degree, brightness standard deviation represents the internal contrast condition, and brightness gradient describes the spatial trend. These indicators together form a multi-dimensional vector describing the brightness perception characteristics, which can effectively reflect the visibility performance of the identification under different lighting conditions.

[0026] The color contrast feature quantization process uses the internationally recognized color difference calculation model. The input image is first converted to a device-independent color space, which can accurately simulate the color perception characteristics of the human visual system. The color difference between the foreground and the background is calculated by an improved color difference formula, which takes into account the perceived differences in brightness, chroma and hue. The main color regions in the image are automatically identified during the analysis process, and a color contrast relationship matrix is established to extract the most representative contrast parameters. For gradient colors or texture regions, an additional consistency indicator of color space distribution is calculated to avoid misjudgment caused by local mutations.

[0027] The text clarity evaluation adopts a multi-scale analysis method. At the pixel level, edge detection operators are applied to extract stroke contour features, and edge sharpness and continuity indicators are calculated. At the regional level, the spatial frequency distribution characteristics of character structures are analyzed to detect blurring or distortion phenomena. Adaptive adjustment of analysis parameter thresholds is performed for different font types and font sizes to ensure the objectivity of the evaluation results. Special processing is performed for text regions under low-contrast conditions to enhance the detection sensitivity of weak signals and avoid missing important information.

[0028] The fusion processing of environmental parameters uses a multi-modal data alignment technique. The intensity data collected by the light sensor is calibrated after standardization and establishes a spatio-temporal correspondence with the image features. The ambient color temperature information is converted into a white balance compensation coefficient for correcting the image color performance. The humidity data participates in the calculation of the atmospheric transmittance model to assist in evaluating the visibility of distant signs. All environmental parameters are verified for validity and filtered for outliers to ensure data reliability. The fusion process of environmental features and visual features uses an attention weighting mechanism to dynamically adjust the fusion weights based on parameter reliability and correlation.

[0029] The organizational structure of the feature function adopts a hierarchical design. The bottom-level features maintain a strict correspondence with the original image pixels, forming a high-precision spatial distribution map. The middle-level features produce more semantically descriptive indicators through regional aggregation, balancing the relationship between details and the whole. The high-level features integrate multi-dimensional information to form a comprehensive evaluation vector. Bi-directional connection paths are established between the features at different levels, supporting both bottom-up feature abstraction and top-down feedback adjustment. This structure enhances the expressive power of feature representation, allowing it to adapt to complex and variable real-world application scenarios.

[0030] The data processing flow includes strict quality control steps. Each analysis step has a complete integrity checkpoint to verify the reasonableness and consistency of the intermediate results. When abnormalities are found, a re-computation mechanism or parameter adjustment strategy is automatically triggered to ensure the robustness of the processing flow. The feature extraction process uses a parallel computing architecture, with different types of features generated simultaneously by dedicated computing units, significantly improving processing efficiency. Memory management uses an intelligent caching strategy that optimizes data access order based on feature dependencies, reducing redundant calculations.

[0031] The output format of the visual ergonomics feature function is carefully designed. The basic feature layer maintains the original spatial resolution, using sparse coding techniques to compress data volume. The statistical feature layer provides regional-level summary indicators for quick analysis of overall conditions. The derived feature layer includes high-level features obtained through nonlinear combination, revealing the interactions between different visual factors. All feature data are accompanied by complete meta-information descriptions, including extraction parameters, processing time, and quality evaluation scores, providing sufficient context for subsequent analysis.

[0032] The feature updating mechanism supports incremental processing. When the input image sequence is input, the system automatically detects the degree of scene change and dynamically adjusts the feature updating frequency. The features of the static region are smoothed using the moving average method to reduce the influence of random noise. The features of the dynamic region are updated in real time to capture the rapidly changing information content. This adaptive updating strategy effectively controls the consumption of computing resources while ensuring the freshness of the features.

[0033] The feature visualization system helps understand complex feature data. A special tool is developed to project multi-dimensional features onto a two-dimensional plane, and the feature distribution law is displayed through color coding and dynamic interaction. The feature importance analysis function is supported to identify key factors that significantly affect the final detection result. The visualization system is used for algorithm debugging and optimization, and also as an auxiliary tool for explaining the detection conclusion, enhancing the transparency and credibility of the system.

[0034] The feature storage scheme adopts a hierarchical archiving strategy. High-frequency features used recently are saved in the cache to support fast retrieval and access. Historical feature data is compressed and stored in a long-term storage system, and a complete version management and indexing mechanism is established. The feature database supports content-based similarity query, which can quickly retrieve identification cases with specific visual characteristics and provide convenience for comparative analysis.

[0035] The feature extraction process fully considers the constraints of practical applications. In a resource-constrained environment, a simplified feature mode is automatically activated to reduce computational load while retaining core evaluation indicators. For different types of identification content, a pre-defined feature configuration scheme is loaded to optimize the processing flow. The system continuously monitors the performance indicators of feature extraction and dynamically adjusts the calculation strategy based on the operating conditions to achieve a reasonable balance between accuracy and efficiency.

[0036] The feature calibration mechanism ensures long-term stability. Standard test samples are used periodically to verify the consistency of feature extraction and detect possible deviations or drifts. A feature benchmark database is established to record standard feature values in typical scenarios as a reference for daily detection. The calibration process automatically adjusts the feature extraction parameters to compensate for the effects of device aging or environmental changes, maintaining the stability of system performance.

[0037] The feature security protection measures prevent data tampering or leakage. Critical feature data is encrypted for storage and transmission, and strict access permission control is set. Feature processing logs record operation information in detail, supporting audit tracking. A feature integrity verification mechanism is established to verify data authenticity using digital signature technology, ensuring the credibility of the detection results.

[0038] Example 2: see Figure 2The specific construction and optimization process of the adaptive detection model framework is described. This framework adopts a multi-layer cascaded structure to achieve intelligent analysis and evaluation of identification information. The data preprocessing layer contains three parallel processing channels, each performing specialized processing for a specific type of input data. The gamma correction channel is responsible for adjusting the brightness response curve of the image to conform to the characteristics of human visual perception; the white balance channel corrects color deviation and eliminates the influence of ambient light color temperature; and the non-uniform illumination compensation channel detects and repairs uneven illumination in the image. The outputs of the three channels are spatially registered through a feature alignment network and then fed into a fusion network for deep integration, producing standardized preprocessed results.

[0039] The feature fusion module consists of multiple cascaded interactive processing units, each with independent feature processing capabilities. The connections between units employ a residual structure design, allowing information to be transferred across layers. The feature encoding component uses multi-scale convolutional kernels to extract spatial features, while simultaneously using dilated convolutions to expand the receptive field. During encoding, the spatial resolution of the feature map remains unchanged, and the number of parameters is reduced through depthwise separable convolutions. The weight optimization component implements dynamic feature selection, generating an attention weight map by analyzing the statistical correlations between features. This component includes two analysis paths: a local path focusing on the relationships between neighboring features, and a global path capturing dependencies between distant features.

[0040] The output synthesis component performs feature recombination operations, transforming the weighted feature maps to a new representation space. This component employs a gating mechanism to control information flow, suppressing the propagation of noisy features and enhancing the representation of useful features. The output of each interactive processing unit is normalized to maintain the stability of the feature distribution. The module includes feature visualization monitoring points that display the activation patterns of key feature maps in real time, aiding in understanding the model's decision-making process.

[0041] The results generation unit is designed with a multi-task learning architecture, simultaneously handling multiple related evaluation tasks. The readability scoring task analyzes the visual clarity of the text, considering factors such as font recognizability, stroke continuity, and background interference; the color recognition task evaluates the rationality of color matching, detecting potential visual conflicts or cognitive obstacles; and the information delivery efficiency task comprehensively considers indicators such as layout rationality, visual guidance, and information density. Each task shares the underlying feature representation but has an independent prediction head, and negative interference between tasks is avoided through feature decoupling techniques.

[0042] The integration of performance objectives employs an adaptive weighting strategy. The system maintains an objective priority queue, dynamically adjusting the weight coefficients of each objective based on user settings or preset standards. When conflicts arise among multiple objectives, a negotiation mechanism is initiated to find a compromise solution. The objective achievement evaluation module continuously monitors the completion status of various indicators and generates optimization direction suggestions. For combinatorial optimization scenarios, the system constructs a multi-dimensional objective space and uses cutting-edge search algorithms to find a set of non-dominated solutions, allowing decision-makers to select the solution that best meets their actual needs.

[0043] The model training process employs a phased optimization strategy. The initial phase focuses on optimizing the shared feature extraction layer, using large-scale pre-training data to enhance the basic feature representation capabilities. The intermediate phase gradually unfreezes the parameters of task-specific layers and introduces a multi-task loss function for joint training. The final phase implements fine-tuning, adjusting the model behavior for specific application scenarios. The training data undergoes rigorous quality screening and augmentation, covering various possible label types and environmental conditions.

[0044] The reasoning process employs multi-level verification. Primary reasoning quickly generates initial assessment results, intermediate reasoning verifies and corrects these results, and advanced reasoning performs comprehensive evaluation. Each reasoning stage includes a confidence assessment step; when the confidence level of the result is insufficient, a more in-depth analysis process is automatically triggered. The system supports an interactive reasoning mode, allowing for human intervention at key decision points and integrating expert experience into the analysis process.

[0045] The model update mechanism enables continuous performance improvements. The online learning module analyzes misclassification cases in real-world applications and extracts representative difficult samples. The active learning strategy identifies unlabeled data with high information content and optimizes the allocation of labeling resources. The model version control system tracks performance changes across versions and supports rapid rollback to stable versions. The update process employs a gradual deployment strategy, first running and validating in shadow mode to confirm performance improvements before official release.

[0046] The resource management subsystem dynamically balances the computational load. The computation graph optimizer analyzes the model structure, eliminates redundant operations, and merges similar computations. The memory allocator employs an intelligent caching strategy, predicting memory requirements based on data access patterns. The parallel computing engine automatically identifies parallelizable subtasks, fully utilizing multi-core processor resources. Under resource constraints, the system automatically activates a simplified inference mode, appropriately reducing computational precision in exchange for processing speed.

[0047] An anomaly handling mechanism ensures system robustness. The input validation module detects and repairs corrupted input data to prevent invalid information from affecting analysis results. The runtime monitor tracks the activation distribution of each layer of the model and triggers protection mechanisms when abnormal patterns are detected. The fault recovery system records key intermediate results, allowing for rapid resumption of the processing flow after unexpected interruptions. The system maintains detailed operation logs, recording all important operations and state changes for easy problem tracing and analysis.

[0048] Explanatory features enhance model transparency. Feature importance analysis tools quantify the contribution of each input feature to the final decision. A decision path visualization system displays the flow and transformation of information within the model. A counterfactual explainer generates modification suggestions, explaining how to change the inputs to obtain different output results. These explanatory tools are used both for model debugging and optimization, and to help users understand the system's decision-making rationale.

[0049] The deployment options support diverse operating environments. The cloud-based version employs a microservice architecture for elastic scaling and load balancing. The edge computing version optimizes model size and inference speed, adapting to resource-constrained devices. The hybrid deployment mode intelligently allocates computing tasks between the cloud and edge, balancing latency and accuracy requirements. Containerization simplifies the deployment process and ensures consistent operating environments. The model conversion tool supports multiple inference frameworks, ensuring cross-platform compatibility.

[0050] The performance monitoring system achieves end-to-end observability. Data acquisition agents collect performance metrics at each processing stage, including processing latency, resource utilization, and result quality. A visual dashboard displays the system status in real time, supporting multi-dimensional performance analysis. An early warning mechanism detects abnormal metric fluctuations and promptly notifies operations personnel for intervention. Long-term performance trend analysis identifies potential system bottlenecks, guiding capacity planning and optimization.

[0051] Security measures ensure system reliability. Model watermarking technology prevents unauthorized model copying and use. Input filtering mechanisms block maliciously crafted adversarial sample attacks. The access control system implements strict permission management to protect sensitive data security. Security auditing functions record all critical operations to meet compliance requirements. Model integrity verification mechanisms regularly detect potential backdoors or deviations to maintain system fairness and reliability.

[0052] Example 3: See Figure 3The system employs a comprehensive mechanism to handle operational constraints and environmental limitations. This mechanism achieves efficient and stable operation through multi-dimensional resource management and adaptive environmental adjustment. Operational constraints are handled using a hierarchical control strategy, dividing system resources into three management domains: computing, storage, and communication. Computing resource management implements dynamic load balancing; when GPU utilization exceeds a preset threshold, a model simplification program is automatically activated. This program removes low-contribution connections from the neural network using structured pruning techniques and employs quantization-aware training to reduce floating-point precision from 32 bits to 16 bits, significantly reducing computation without significantly affecting model accuracy. Storage management uses an intelligent cache replacement algorithm; frequently accessed feature data is retained in a high-speed cache, while historical data is compressed and encoded before being stored in secondary storage. The compression process uses a lossy compression strategy based on feature importance, preserving the full accuracy of key features while allowing moderate distortion for secondary features.

[0053] Latency control incorporates a real-time scheduling mechanism, establishing a task priority queue. High-priority tasks receive more computing resources, while regular tasks are executed using a time-slice round-robin approach. A latency monitor continuously tracks the processing time of each stage, initiating an emergency response process when the processing time of a single frame approaches its limit. This process first reduces image resolution using content-aware downsampling to maintain high clarity in text and graphic areas while strongly compressing uniform background areas. The latency optimization process follows these resource allocation principles: ; in, This indicates the actual amount of resources allocated. Represents the task priority coefficient. This represents the total amount of available resources in the current system. Let be the weight factor for the i-th task. This represents the historical resource consumption rate of the task. This formula dynamically balances resource competition among tasks, ensuring that critical tasks receive sufficient resources while preventing low-priority tasks from experiencing prolonged starvation.

[0054] The environmental constraint processing system constructs a multi-sensor data fusion pipeline. Ambient light intensity data collected by the illumination sensor is calibrated and converted into image enhancement parameters. The conversion process considers the brightness adaptation characteristics of human vision, with enhancement greater in dark environments than in bright environments. Viewpoint angle data comes from the inertial measurement unit; the relative angle between the observation plane and the marker plane is calculated through coordinate transformation, and this angle is used for perspective distortion correction. Background interference analysis employs a two-stage processing flow: first, a semantic segmentation network separates the foreground marker from the background content; then, the visual complexity index of the background region is calculated. All environmental parameters undergo temporal smoothing filtering to eliminate instability caused by instantaneous fluctuations.

[0055] An environmental adaptation mechanism enables dynamic adjustment of processing parameters. Changes in lighting conditions trigger parameter updates in the image preprocessing pipeline, including gamma values, contrast enhancement intensity, and local histogram equalization. When the viewpoint change exceeds a threshold, the system activates the geometric correction module, applying perspective transformation to restore the frontal plane of the sign. Changes in background interference affect the attention distribution of feature extraction, with foreground features receiving increased weight in highly interference environments. The fusion of environmental parameters and visual features employs a conditional normalization method, where environmental factors act as modulation signals, influencing the feature normalization process.

[0056] The resource monitoring system provides full-stack visualization. The computing resource dashboard displays real-time curves of processor utilization, memory usage, and GPU memory consumption. The storage monitoring interface displays cache hit rate, data throughput, and storage space usage. The environment status dashboard integrates sensor readings, processing parameter adjustment records, and comparisons of environmental adaptation effects. All monitoring data retains historical records, supporting time-range queries and trend analysis. The anomaly detection module identifies abrupt changes in resource usage patterns, providing early warnings of potential system bottlenecks.

[0057] Constraint conflict resolution employs a priority arbitration mechanism. When multiple constraints cannot be satisfied simultaneously, they are prioritized according to a preset priority order. When computational resources are insufficient, priority is given to ensuring the operation of core detection functions, suspending auxiliary analysis tasks. When storage space is tight, intermediate result caches are cleared first, retaining original input and final output data. Under harsh environmental conditions, basic visibility assurance is prioritized, temporarily ignoring advanced aesthetic evaluation. Arbitration decisions are recorded in the system log for subsequent analysis and strategy optimization.

[0058] The dynamic configuration system supports flexible adjustment of runtime parameters. Operational limit thresholds are updated in real time via configuration files and take effect without requiring a service restart. Environmental constraints can be preset with multiple schemes based on application scenarios, automatically switching according to geographical location or time. The parameter adjustment interface provides intuitive slider controls and displays a comparison of the expected effects before and after modification. Changes to important parameters require double confirmation to prevent accidental operations from affecting system stability.

[0059] The fault-tolerance mechanism handles extreme environmental conditions. When the light intensity exceeds the sensor's range, a brightness estimation method based on image content is activated. In case of abnormal viewing angles, it reverts to a geometric correction mode based on content analysis. When background interference analysis fails, a conservative feature enhancement strategy is employed. All fault-tolerance processes log abnormal events and issue a notification once environmental conditions return to normal. The system periodically performs a self-test procedure to verify the functional integrity of each environmental adaptation module.

[0060] Version compatibility management supports the gradual evolution of constraints. Older configuration files can be automatically converted to the new format, maintaining setting continuity. The environmental parameter database is designed with a scalable structure to accommodate newly added sensor data types. The resource monitoring indicator system supports the dynamic addition of new metric dimensions without affecting existing monitoring functions. This design ensures the system can adapt to hardware upgrades and environmental changes during long-term operation.

[0061] Co-optimization of environmental constraints and operational limitations is achieved through a feedback control loop. The resource allocation strategy considers current environmental conditions, allocating more computational resources to the preprocessing stage in complex environments. Environmental parameter adjustments consider system resource availability, employing more computationally efficient simplified algorithms when resources are scarce. The co-optimizer periodically analyzes the correlation between resource usage and environmental conditions, automatically adjusting control parameters to form a virtuous cycle of adaptation. The system maintains a decision knowledge base, recording optimization strategies and their effects under various scenarios to provide a reference for future decisions.

[0062] The safety boundary protects the system from extreme conditions. Hard limits are set on resource usage to prevent a single task from exhausting all computing power. Environmental parameter settings are validated within their effective range to filter out obviously abnormal sensor readings. All adaptive adjustments have maximum variation limits to avoid drastic parameter oscillations. The system retains a set of safe default parameters, automatically reverting to a basic protection mode in the event of persistent anomalies. These protective measures together constitute the defense system, maintaining its basic functionality under various adverse conditions.

[0063] Environmental modeling tools aid in understanding complex constraints. 3D scene reconstruction visualizes the spatial relationship between the observer and markers. A lighting simulator predicts natural light variations over different time periods. A background pattern analyzer identifies periodic interference sources. These tools help users anticipate potential environmental challenges and prepare accordingly. The environmental model data accumulated by the system can be used for offline analysis and solution validation, optimizing on-site deployment strategies.

[0064] A cross-platform adaptation layer handles differences in hardware environments. On devices with limited computing power, it automatically selects optimized lightweight model variants. For situations where specific sensors are missing, it enables software simulation mode to provide alternative data sources. The storage architecture automatically selects the most suitable implementation based on device type; for example, mobile devices employ more aggressive memory compression strategies. This adaptability expands the system's applicable scenarios, enabling it to maintain stable performance on diverse hardware platforms.

[0065] Example 4: See Figure 4The system demonstrates the complete processing flow of sign content feature representation and detection result evaluation, showcasing the actual operation of the dual-path coding scheme and three-level verification mechanism through specific examples. Taking the detection scenario of a road traffic sign "Speed ​​Limit 60 km / h" as an example, the system first acquires the sign image through a high-resolution camera. The raw data is a RAW format file of 4096×2160 pixels, while simultaneously recording the ambient light intensity as 85000 lux and the viewing angle as a 15-degree downward angle.

[0066] In the pixel matrix mapping process, the image is fed into an improved U-Net network for multi-scale feature extraction. The first-stage downsampling outputs a feature map with a resolution of 2048×1080, capturing the overall contour and positional information of the logo; the second-stage output is a 1024×540 feature map, extracting the structural features of the text region; the third-stage output is a 512×270 feature map, analyzing stroke details and color boundaries. The upsampling process uses skip connections to fuse features from different scales, ultimately generating a 256-dimensional spatial feature vector, which contains structured information such as the logo's geometry and text layout.

[0067] The visual ergonomic feature function pathways process the same input image in parallel. The brightness distribution feature calculates the average brightness of the signage area to be 72 cd / m², with a brightness contrast ratio of 5:1. The color contrast feature measures the color difference ΔE between red text on a white background to be 8.7. The text clarity index, obtained through edge sharpness analysis, yields a clarity score of 86 (out of 100). These features are compressed through a fully connected layer to form another 256-dimensional semantic feature vector. The dual-path features are then concatenated in a fusion layer to form a 512-dimensional comprehensive feature representation, fully describing the visual attributes and ergonomic characteristics of the signage.

[0068] A three-tiered verification mechanism progressively verifies the compliance of the markings. The initial assessment outputs an overall compliance score of 82 points, with brightness contrast scoring 18 / 20, color recognition scoring 20 / 25, and text readability scoring 44 / 55. The intermediate assessment generates a specific set of defect markers, as shown in Table 1.

[0069] Table 1: Intermediate assessment generates a specific set of defect tags.

[0070] ; The optimization recommendation vector generated by the advanced assessment includes three specific measures: it is recommended to adjust the installation angle of the sign to reduce the downward angle to within 10 degrees; it is recommended to use anti-glare coating materials; and it is suggested that increasing the red saturation by 15% under existing lighting conditions can achieve better visual recognition.

[0071] The hybrid architecture for content compliance analysis works collaboratively. The feature matching engine compares the sign to the GB5768-2022 road sign specification in the standard template library, finding that the dimensions are fully compliant but the reflectivity is slightly below the standard value. The pattern recognizer uses a support vector machine to classify the sign as a "speed limit sign" with 92% confidence. The neural network classifier analyzes subtle image features and detects minor physical damage in the lower right corner, which may affect nighttime visibility. The three analysis results are weighted and fused to generate a final deviation metric of 0.18, indicating that the sign basically meets the standard but has room for optimization.

[0072] A two-way data transfer mechanism enables dynamic interaction between features and evaluation results. During forward propagation, pixel-level features primarily affect defect localization accuracy, while ergonomic features dominate the overall score calculation. During backpropagation, feature regions with higher compliance scores receive stronger gradient signals, guiding the model to focus on key quality factors. An interpretable correlation is established between feature representations and detection results; for example, color shift defects are mainly related to the color contrast feature channel, while stroke defects are reflected in abnormal activation of the texture feature channel.

[0073] In practical highway inspection applications, the system handles a detection task containing 200 traffic signs. When the data collection vehicle travels at 60 km / h, the system's average processing time per sign is 320 milliseconds, including 180 milliseconds for feature extraction, 90 milliseconds for compliance analysis, and 50 milliseconds for result generation. Typical highway directional sign analysis results show that the green background and white text color scheme achieves high color recognition scores, but the directional arrows at some complex intersections are deemed to require layout optimization due to viewing angle issues.

[0074] The system demonstrates excellent adaptability to the unique challenges of urban road environments. When detecting a no-parking sign partially obscured by tree branches, the complementary features of the dual-path system come into play: the pixel path identifies the obscured red circular outline, while the ergonomic path infers the presence of the complete sign based on the color and brightness characteristics of the visible portion. A three-level verification mechanism provides an assessment conclusion of "partial obscuration needs to be cleared," rather than directly deeming it unacceptable.

[0075] The evaluation case of outdated signage demonstrates the system's sophisticated analytical capabilities. A warning sign, over five years old, while structurally intact, showed a 30% degradation in reflective material performance through advanced feature analysis. The system not only marked the current condition but also predicted that, at the current rate of degradation, the sign might fail to meet minimum reflectivity requirements in eight months, generating preventative maintenance recommendations.

[0076] In tests conducted under tunnel lighting conditions, the system automatically adapted to low-light conditions. When testing an emergency exit sign with an illuminance of only 150 lux, the preprocessing of the pipe enhancement image signal-to-noise ratio was performed, and feature extraction focused on brightness contrast analysis. The evaluation results indicated that while the sign's green fluorescent coating met the standards for use in dark environments, its relatively high installation height might affect visibility in emergency situations.

[0077] The multilingual sign detection example demonstrates the system's generalization ability. When processing a bilingual (Chinese and English) road construction sign, the text clarity assessment separately addresses the text regions of both languages. The feature matching engine simultaneously calls template libraries from national standards and ISO standards for comparison to ensure the correct recognition of internationally recognized symbols. The assessment results indicate that the font spacing in the English portion is slightly smaller than the recommended value, which may affect long-distance readability.

[0078] The system's iterative optimization process is continuously improved through real-world case studies. When multiple similar signs are detected exhibiting similar reflectivity issues, the deviation measurement and analysis module automatically adjusts the weights of relevant features to enhance sensitivity to signs of material aging. Simultaneously, the standard template library is updated, and technical parameters of new reflective materials are added to maintain the timeliness of the evaluation benchmark.

[0079] The quality control log records complete inspection process data. Each identified inspection report includes the original image, feature heatmap, evaluation score, and optimization recommendations. Historical data is archived by road segment and time, supporting quality trend analysis and maintenance priority sorting. The anomaly detection function automatically identifies outliers in the evaluation results, alerting to potential special conditions or system misjudgments.

[0080] The user feedback mechanism improves the evaluation criteria. When maintenance personnel raise objections to the evaluation results of a certain sign, the system records the manual review conclusion and analyzes the reasons for the discrepancy. If it is confirmed that the discrepancy is due to a deviation in the evaluation criteria, the model parameter adjustment process is initiated to optimize the detailed judgment criteria while maintaining the core principles. This interactive learning allows the system to continuously adapt to actual application needs.

[0081] Example 5: See Figure 5The system outlines the specific operational process for determining stability conditions and adjusting optimization mechanisms. This process achieves self-improvement and stable output of the detection system through dynamic convergence criteria and multi-strategy collaborative optimization. The convergence determination module employs a dual monitoring strategy to continuously track the changing trend of the deviation metric. The sliding window analyzer maintains a circular buffer of length K, recording the deviation sequence of the most recent K iterations. After standardization, the data within the window is used to calculate two key indicators: the moving average and the standard deviation. The convergence condition not only requires the standard deviation of the current window to be lower than the static threshold but also requires observation of the downward trend over N consecutive windows to avoid false convergence caused by local extrema. The upper limit of the number of iterations is set as an elastic variable, with an initial default value of 100 iterations. The system dynamically adjusts this value based on historical convergence patterns. When periodic oscillations are detected, the upper limit of iterations is automatically extended and the convergence threshold is relaxed, providing more sufficient search space for the optimization algorithm.

[0082] The optimization mechanism integrates three complementary search strategies to form a hybrid optimization system. Adaptive learning rate adjustment technology serves as the basic optimizer, monitoring the historical changes in the gradients of each parameter and dynamically adjusting the learning step size. Increasing the learning rate in flat regions of the parameter space accelerates progress, while decreasing it in steep, challenging regions avoids oscillations. A swarm intelligence algorithm introduces diverse search capabilities, maintaining a set of candidate solutions to explore different regions of the parameter space in parallel. Candidate solutions communicate and discover each other through an information-sharing mechanism, gradually focusing on the most promising regions. A metaheuristic search strategy provides global escape capability; when optimization is detected to be stalled, a temporary performance degradation is accepted with a controllable probability to avoid premature convergence. The three strategies are coordinated and assigned tasks by a controller, dynamically adjusting the participation intensity of each strategy according to the characteristics of the current optimization stage.

[0083] The parameter update process employs a hierarchical management strategy. Low-level parameters primarily influence the fine-grained characteristics of feature extraction, using a smaller learning rate and a more refined update strategy. Mid-level parameters control the interactions between features, with an update frequency set to once every 5 iterations, allowing sufficient stabilization time to observe the adjustment effects. High-level parameters determine the overall behavior pattern of the system, employing a conservative update strategy, with a comprehensive impact assessment performed before each modification. A mutual constraint mechanism is implemented for the update magnitudes of parameters at each level: drastic changes in low-level parameters trigger recalibration of mid-level parameters, and significant adjustments to high-level parameters require simultaneous verification of low-level parameters. This hierarchical update structure maintains system stability while allowing parameter evolution at different time scales.

[0084] The feedback loop design incorporates multiple verification mechanisms. After each parameter update, the system evaluates the model performance changes on an independent validation set, filtering out spurious improvements from overfitted training data. The verification process not only checks changes in the overall bias metric but also analyzes the performance balance of each subtask to prevent unfair bias between tasks during optimization. For parameter modifications involving safety-critical functions, additional boundary condition testing and adversarial example testing are performed to ensure that the modifications do not introduce new vulnerabilities. Parameter updates that pass verification are marked as trustworthy changes and synchronously updated to the production environment; modifications that fail trigger a rollback mechanism, reverting to the previous stable version.

[0085] The dynamic adjustment algorithm for the convergence threshold considers multiple factors. The base threshold gradually tightens according to an inverse proportional function based on the current iteration number. Larger fluctuations are allowed in early iterations, while precise convergence is required in later iterations. In practical applications, the threshold also needs to be adjusted by a correction factor reflecting the achievement of performance targets. Requirements are relaxed for subtasks that are close to achieving the targets, focusing on optimizing metrics with significant gaps. Changes in environmental conditions also affect the threshold setting; in complex environments, accuracy requirements are appropriately reduced to prioritize the reliability of basic functions. The threshold adjustment process itself has safety limits, with each adjustment not exceeding 30% of the previous value to avoid parameter oscillations.

[0086] The optimization process monitoring system provides panoramic visualization. A 3D parameter space projection displays the current solution's location and neighborhood exploration status, using color coding to distinguish regions discovered by different strategies. Performance change curves simultaneously show metric trends on both the training and validation sets, highlighting overfitting risk points. A resource consumption dashboard tracks the computational load percentage of each optimization strategy, guiding resource allocation adjustments. The change paths of key parameters are recorded as source maps, helping to understand the evolution logic of system behavior. Monitoring data supports interactive exploration, allowing expert intervention at key decision points, injecting domain knowledge into the automated optimization process.

[0087] The anomaly handling system maintains the safety boundaries of the optimization process. When a parameter value is detected to be outside the reasonable range, a constraint enforcement mechanism is automatically triggered to pull the out-of-bounds parameter back into the valid range. If optimization stalls for more than a predetermined time threshold, a diversity injection program is initiated to reset the search direction of some candidate solutions. A resource monitor prevents the optimization process from exhausting system memory or computing resources, pausing secondary tasks to ensure core functions when necessary. All anomalies generate detailed diagnostic reports, including triggering conditions, system responses, and follow-up suggestions, accumulating to form a fault mode knowledge base.

[0088] The version management subsystem supports the repeatability of the optimization process. Each parameter update generates a unique version hash value, fully recording parameter status and performance metrics. The version rollback function allows for quick restoration to any historical point if an optimization error is discovered. Branch management allows for parallel testing of multiple parameter hypotheses, comparing the effects of different optimization paths through A / B testing. The version difference analysis tool visualizes the cumulative impact of parameter changes, helping to understand causal relationships in complex systems.

[0089] The meta-learning mechanism for optimizing strategies enables algorithm self-improvement. The system records the effectiveness data of different strategies under various optimization scenarios, constructing an experience base for strategy selection. When encountering a new optimization problem, it recommends the most likely successful strategy combination based on similarity matching. Policy parameters also participate in the optimization process, such as adjusting the explore-exploitation balance coefficient of the swarm intelligence algorithm to form a customized optimizer adapted to the characteristics of specific problems. This meta-learning capability enables the system to quickly adjust its optimization methods when dealing with novel identifiers or special environments.

[0090] Long Term Evolution (LTE) systems achieve continuous performance improvements. Deployed models absorb feedback data from real-world applications through online learning mechanisms, continuously fine-tuning parameters to adapt to real-world scenario distributions. A concept drift detection module identifies gradual changes in environmental conditions or labeling standards, indicating signals that require re-optimization. Knowledge distillation technology transfers experience from large-scale optimization models to lightweight versions, maintaining processing power on edge devices. Regularly executed global optimization processes integrate scattered incremental improvements, preventing system parameter fragmentation.

[0091] A robust stability assurance system prevents optimization from introducing systemic risks. Simulation tests before parameter modifications verify behavioral consistency in critical scenarios, and major updates are released gradually in a phased, canary rollout. Strictly isolated A / B testing is conducted in the production environment to compare old and new versions, confirming the improvement effects before a full switch. Rollback contingency plans are pre-designed for each important parameter update to ensure rapid service recovery in case of unforeseen circumstances. This robust deployment strategy pursues performance improvements while maintaining system operational security and business continuity.

[0092] The human-machine collaborative optimization interface supports expert guidance. Visualization tools display current parameter optimization bottlenecks and opportunities, allowing manual annotation of key search areas. The strategy configuration panel provides fine-grained control options to adjust the internal parameters of each optimization algorithm. Intervention logs fully record the operational steps and decision-making basis of human experts. This collaborative model retains the efficiency advantages of automatic optimization while incorporating the deep knowledge of domain experts, forming a complementary optimization community.

[0093] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0094] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A visual ergonomics-based identification information content intelligent detection method, characterized in that, The method comprises the following steps: Collecting identification visual data to obtain high-resolution images of target identification and related environmental parameters; Based on the collected identification visual data, a set of visual ergonomics feature functions are extracted, including brightness distribution, color contrast, and text legibility indicators; Using the extracted visual ergonomics feature functions, an adaptive detection model framework is constructed; According to the performance target, operation limit and environmental constraint of the detection task, an iterative optimization process is performed on the adaptive detection model framework, which specifically includes: Input the feature representation of the identification content into the adaptive detection model framework, and output the detection result evaluation value; Performing content compliance analysis on the output detection result evaluation value to generate a deviation metric value; Determine whether the deviation metric value meets the preset stable condition, if yes, output the final detection conclusion and terminate the process, if not, perform parameter updating operation; According to the deviation metric value and the optimization mechanism, the internal parameters of the adaptive detection model framework are adjusted, and the above input, analysis, judgment and updating operations are repeated until the preset stable condition is met.

2. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The visual ergonomics feature functions are obtained by processing the block region of the identification image, each feature function is expressed as a multidimensional data array, and the array size is directly related to the image pixel density. Environmental parameters are integrated into feature calculation during processing to enhance data dimension.

3. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The adaptive detection model framework integrates data preprocessing layer, feature fusion module and result generation unit, wherein the feature fusion module is composed of multiple interactive processing units, each interactive processing unit includes feature encoding component, weight optimization component and output synthesis component; the feature encoding component encodes the key information of the input identification visual data using the visual ergonomics feature function to generate a set of encoded features; the weight optimization component applies a dynamic adjustment mechanism to assign importance to the set of encoded features; the output synthesis component integrates the weighted feature set and passes it to the result generation unit.

4. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The performance target of the detection task includes one or more combinations of identification readability improvement target, color recognition optimization target, and information transmission rate maximization target, which is defined by user input or preset standard.

5. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The operation limit includes one or more of the upper limit of processing delay, the threshold of computing resource consumption, and the boundary of storage capacity, which is imported through a system configuration interface and affects the training efficiency of the adaptive detection model framework.

6. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The environmental constraint involves one or more of the environmental light intensity range, the observer's viewing angle range, and the background interference degree, and the environmental constraint data is collected from sensors and interacts with the visual ergonomics feature function to correct the input of the adaptive detection model framework.

7. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The feature representation of the identification content includes pixel matrix mapping or visual ergonomics feature function; the detection result evaluation value includes compliance score, defect label set or optimization suggestion vector, and bidirectional data transfer is realized between the feature representation and the detection result evaluation value through the adaptive detection model framework.

8. The visual ergonomics-based identification information content intelligent detection method according to claim 1, characterized in that, The content compliance analysis is performed using image processing technology, including one or more of feature matching engine, pattern recognizer or neural network classifier, and the analysis process receives the detection result evaluation value and outputs the deviation metric value for subsequent optimization.

9. The visual ergonomics-based identification information content intelligent detection method according to claim 1, wherein, The preset stable condition is defined as triggering termination when a continuous change amplitude of the bias metric value maintains below a convergence critical value, or an iteration execution number exceeds a preset upper limit, and the convergence critical value is dynamically adjusted by the performance target.

10. The visual ergonomics-based identification information content intelligent detection method according to claim 1, wherein, The optimization mechanism includes one or more of an adaptive learning rate adjustment technique, a swarm intelligence algorithm, or a meta-heuristic search strategy, and the optimization mechanism updates the parameters based on the bias metric value and feeds back to the adaptive detection model framework.

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