Intelligent part identification and validation using digital maps

Abstract

A computer-implemented system and method are provided for validating a manufactured part using a component-aware, map-driven architecture. One or more processors access a structured digital map of a reference part encoding representations of multiple components and expected spatial relationships. Image data representing a manufactured instance is received and individual components are detected. A geometric transformation is computed to align detected component locations with expected component locations defined by the structured digital map to establish component-wise correspondence. Component-specific validation metrics are computed based on comparisons between image data associated with detected components and corresponding reference information defined by the structured digital map. The metrics are aggregated to generate a structured part-level validation result comprising a conformity determination relative to the reference part. Systems and non-transitory computer-readable media configured to perform the method are also disclosed.

Description

INTELLIGENT PART IDENTIFICATION AND VALIDATION USING DIGITAL MAPS

[0001] CLAIM OF PRIORITY

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 764,201, filed February’ 27, 2025, entitled “Computer-Implemented Methods for Training Image Generation, Specification Extraction, Intelligent Part Identification, and Searching Equipment Library’ Systems.”

[0003] INCORPORATION BY REFERENCE

[0004] The entire disclosure of U.S. Provisional Patent Application No. 63 / 764,201, filed February 27, 2025. is incorporated herein by reference in its entirety for all purposes.

[0005] Any patents, patent applications, publications, or other references cited herein are incorporated by reference to the extent permitted by applicable law and to the extent not inconsistent with the present disclosure.

[0006] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0007] Not Applicable.

[0008] NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT

[0009] Not Applicable.

[0010] INCORPORATION OF MATERIAL SUBMITTED ON A COMPACT DISC OR AS A TEXT FILE VIA EFS-WEB

[0011] Not Applicable.

[0012] SEQUENCE LISTING STATEMENT

[0013] Not Applicable.

[0014] REFERENCE TO A SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTING APPENDIX

[0015] Not Applicable.

[0016] STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR

[0017] Not Applicable.

[0018] 1. FIELD OF THE INVENTION

[0019] The present disclosure relates generally to computer-implemented inspection systems and, more particularly, to component-aware, map-driven validation of manufactured parts using computer vision techniques.

[0020] 2. BACKGROUND

[0021] Inspection of manufactured parts is commonly performed to determine whether a manufactured instance conforms to a reference configuration. In many industries, such inspection relies on analysis of captured image data representing a manufactured part.

[0022] Certain image-based inspection approaches perform global comparison between a captured image and a reference image or template. Such approaches may involve aligning images and identifying differences at a pixel or region level. While effective in certain controlled environments, global image comparison techniques can be sensitive to lighting variation, pose variation, occlusion, and other environmental factors.

[0023] Other approaches may detect defects after performing image registration, but may not enforce explicit correspondence between individual components of a manufactured part and expected component definitions associated with a reference configuration.

[0024] In environments in which multiple components must be validated simultaneously, and where components may vary in importance or allowable deviation, global inspection techniques can lack structural precision. In particular, systems that rely primarily on wholeimage comparison may not reliably determine whether specific components are present, properly positioned, or conforming to expected attributes.

[0025] Accordingly, there exists a need for improved computer-implemented inspection architectures capable of validating manufactured parts in a manner that enforces structured correspondence between detected components and a defined reference representation.

[0026] 3. SUMMARY

[0027] In one aspect, the present disclosure provides a computer-implemented method for validating a manufactured part by enforcing structured correspondence between a manufactured instance and a defined reference configuration.

[0028] The method includes accessing, by one or more processors, a structured digital map of a reference part. The structured digital map encodes representations of a plurality of individual components of the reference part and expected spatial relationships among the plurality of individual components. In certain embodiments, the structured digital map further encodes component identifiers, reference imagery associated with individual components, reference textual or symbolic information associated with individual components, and positional constraints defining allowable deviations relative to expected spatial relationships.

[0029] The method further includes receiving image data representing a manufactured instance of the part. The image data may be captured using a camera-equipped device, including a mobile device, or may be received from another imaging system. In some embodiments, the structured digital map is generated based on image data of a reference manufactured part.

[0030] From the received image data, a plurality of individual components of the manufactured instance are detected. In various embodiments, detection may be performed using one or more trained object detection models, image segmentation techniques, or other computer vision methods capable of identifying regions corresponding to individual components. Detected component regions may be extracted for further analysis.

[0031] Expected component locations corresponding to the plurality of individual components are determined based on the structured digital map. A geometric transformation is computed to align locations of the detected individual components with the expected component locations defined by the structured digital map. In certain embodiments, the geometric transformation includes computation of a transformation matrix. The transformation may include one or more of translation, rotation, scaling, affine transformation, or other geometric operations. In some embodiments, the transformation matrix is computed to minimize spatial error between detected component locations and expected component locations. In certain embodiments, spatial error may be represented as a cumulative error metric and minimized using a leastsquares criterion or other optimization technique. In some embodiments, the conformity determination may be generated based on evaluation of spatial deviation relative to positional constraints encoded in the structured digital map.

[0032] The geometric transformation is computed using the structured digital map as a reference framework and enforces component-wise correspondence between detected components of the manufactured instance and components represented in the structured digital map. Establishing correspondence may include associating each detected component with a specific component identifier defined in the structured digital map.

[0033] For individual ones of the detected components, respective component-specific validation metrics are computed, each comprising a value indicative of conformity or deviation relative to the structured digital map, based on comparisons between image data associated with each detected component and corresponding reference information defined by the structured digital map. In various embodiments, component-specific validation may include template matching between detected component imagery and reference imagery; extraction and comparison of textual or symbolic features derived from image data associated with detected components, including via optical character recognition (OCR) or other image-based feature extraction techniques; or combinations thereof.

[0034] Component-specific validation metrics are aggregated to generate a structured part-level validation result comprising a conformity determination relative to the reference part. In certain embodiments, the conformity determination is based on comparison of an aggregate score to a predetermined threshold. In some embodiments, aggregation comprises computing aweighted aggregate score based on weighting values assigned to individual components to reflect differing conformity significance.

[0035] The structured part-level validation result may include identification of one or more specific components determined to be non-conforming. In some embodiments, a visual overlay is generated identifying one or more non-conforming components within the image data. The visual overlay may include graphical indicators corresponding to deviation magnitude or deviation type.

[0036] In another aspect, a system for validating a manufactured part is provided. The system includes one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the processors to perform operations corresponding to the method described above, including accessing the structured digital map, detecting individual components, computing a geometric transformation using the structured digital map as a reference framework to enforce componentwise correspondence, computing component-specific validation metrics, and aggregating the metrics into a structured part-level validation result.

[0037] In a further aspect, a non-transitory computer-readable medium is provided storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method described herein.

[0038] The disclosed architecture thus provides a component-aware, map-driven inspection system in which validation is performed at the level of individual components and enforced through alignment using a structured digital map as a reference framework, rather than through unconstrained global image comparison.

[0039] 4. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] FIG. 1 is a system diagram illustrating an example system for implementing component-aware validation of manufactured parts, according to various example embodiments.

[0041] FIG. 2 is a block diagram illustrating an example electronic device for implementing aspects of the disclosed systems and methods, according to various example embodiments.

[0042] FIG. 3 is a schematic diagram illustrating an example structured digital map of a reference part, including a map-defined coordinate system, according to various example embodiments.

[0043] FIG. 4 is a flowchart illustrating an example validation process for validating a manufactured part, according to various example embodiments.

[0044] FIG. 5 is a schematic diagram illustrating an example alignment and component correspondence configuration including cumulative spatial error minimization, according to various example embodiments.

[0045] FIG. 6 is a schematic diagram illustrating an example validation metric computation and conformity determination architecture including aggregation of componentlevel metrics and threshold-based conformity classification, according to various example embodiments.

[0046] FIG. 7 is a schematic diagram illustrating an example validation output interface including visual representation of a manufactured part, overlay indicators identifying nonconforming components, deviation magnitude indicators, component-specific scores, and an aggregate validation score, according to various example embodiments.

[0047] 5. DETAILED DESCRIPTION

[0048] 5.1 Example System Environment (FIG. 1)

[0049] Referring now to FIG. 1, components of one embodiment of an environment in which the present disclosure may be practiced are illustrated. Not all components may be required to practice the present disclosure, and variations in the arrangement and type of components may be made without departing from the spirit or scope of the present disclosure.

[0050] As shown, system 100 may include one or more local area networks (LANs) and / or wide area networks (WANs) 112, one or more wireless networks 110, one or more wired or wireless client devices 106, mobile or other wireless client devices 102-105, servers 107-109, and an application hosting sen' er 113. The system 100 may further include or communicate with one or more data stores or databases.

[0051] Client devices 102-106 may include, for example, desktop computers, laptop computers, augmented reality / virtual reality / mixed reality (XR) headsets, set-top boxes, tablet devices, cellular telephones, smart phones, smart speakers, wearable devices (e.g., smart watches), handheld computing devices, GPS-enabled devices, sensor-equipped devices, and other computing devices capable of sending and receiving data over a wired and / or wireless network.

[0052] Client devices 102-106 may run a variety of operating systems, including personal computer operating systems such as Windows, macOS, or Linux, and mobile operating systems such as iOS. Android, or other mobile platforms. Client devices may execute one or more applications configured to send and receive data from servers 107-109 or application hosting server 113.

[0053] Servers 107-109 and application hosting server 113 may include application servers, content servers, search servers, cloud servers, database servers, or other computingsystems capable of sending, receiving, processing, and storing data. Servers may include dedicated rack-mounted servers, desktop computing devices, laptop computing devices, integrated devices, or combinations thereof.

[0054] A server may include one or more central processing units, memory, mass storage, power supplies, wired or wireless network interfaces, and operating systems such as Windows Server, macOS Server, Unix, Linux, FreeBSD, or others. Servers may provide sendees including web services, application services, data storage services, search sen ices, HTTP / HTTPS senices, messaging services, file transfer services, and other services.

[0055] Networks 110 and 112 may include the Internet. LANs, WANs, wireless networks, cellular networks, direct connections, optical networks, satellite links, DSL connections, cable connections, integrated services digital networks (ISDN), Bluetooth, Wi-Fi (e.g., IEEE 802.11 variants), LTE, LTE- Advanced, GSM, UMTS, EDGE, WCDMA, or other communication technologies.

[0056] Communication may be performed using Internet Protocol (IP), including IPv4 or IPv6, and protocols such as TCP / IP, UDP, FTP, HTTP, HTTPS, SMS, MMS, VoIP, or other protocols.

[0057] In certain embodiments, system 100 may operate in conjunction with distributed architectures such as content delivery networks (CDNs), cloud computing environments, peer-to-peer networks, or hybrid network architectures.

[0058] The configuration illustrated in FIG. 1 is exemplary and non-limiting.

[0059] 5.2 Example Electronic Device (FIG.2)

[0060] Referring now to FIG. 2, an example electronic device 200 capable of implementing one or more aspects of the disclosed systems and methods is illustrated.

[0061] Electronic device 200 may include a processor 202, memory 230, communications bus 204, power supply 206, network interface(s) 214, and input / output interface(s) 240.

[0062] Processor 202 may include one or more processing devices, such as a central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuit (ASIC), programmable logic device, microprocessor, or combinations thereof. Processor 202 may execute hardware, firmware, and / or software instructions to perform functions described herein.

[0063] Memory 230 may include random access memory (RAM) 212 and read-only memory (ROM) 232. RAM 212 may store operating system 221, programs and / or applications 222, data storage 224 (which may include one or more databases), and program logic 223implementing aspects of the systems and methods described herein. ROM 232 may store basic input / output system (BIOS) 220.

[0064] Communications bus 204 may facilitate communication between components of electronic device 200.

[0065] Network interface(s) 214 may include receivers, transmitters, or transceivers configured for wired or wireless communication, including communication over networks 110 and 112.

[0066] Input / output interface(s) 240 may include interfaces for facilitating communication between components of electronic device 200 and external devices, including displays, touchscreens, keyboards, mice, cameras, microphones, speakers, sensors, GPS components, biometric sensors, or other peripheral devices.

[0067] Electronic device 200 may represent a client device 102-106, a server 107-109, application hosting server 113. or another computing device within system 100.

[0068] Electronic device 200 may be capable of sending or receiving signals via a wired or wireless network and may process or store signals as physical memory states within memory 230.

[0069] The configuration illustrated in FIG. 2 is exemplary and non-limiting.

[0070] 5.3 Overview of Component-Aware Inspection Architecture

[0071] Referring generally to FIGS. 3-7, the present disclosure provides a component-aware, map-driven inspection architecture for validating a manufactured part relative to a defined reference configuration.

[0072] In various image-based inspection environments, validation may be performed by aligning a captured image with a reference image and evaluating differences at a global or region level. Such approaches may rely on whole-image comparison or unconstrained defect detection following image registration. While such techniques may identify certain visual differences, they do not necessarily enforce structured correspondence between specific components of a manufactured part and defined component representations within a reference configuration.

[0073] In contrast, the architecture described herein is organized around a structured digital map of a reference part and operates at the level of individual components rather than solely at a whole-image level.

[0074] The structured digital map encodes representations of individual components and expected spatial relationships among those components. Validation is performed by detecting individual components within image data of a manufactured instance, aligning detected component locations to expected component locations defined in the structured digital map, and enforcing component-wise correspondence prior to computing validation metrics.

[0075] By establishing correspondence relative to the structured digital map, the disclosed architecture evaluates whether specific components are present, properly positioned, and conforming to expected characteristics, rather than merely identifying global pixel-level deviations.

[0076] Furthermore, validation metrics are computed separately for individual components and subsequently aggregated into a structured part-level validation result. This aggregation architecture allows component-level determinations to contribute to an overall conformity assessment in a structured and interpretable manner.

[0077] In certain embodiments, enforcement of component-wise correspondence relative to the structured digital map improves robustness to lighting variation, pose variation, partial occlusion, and environmental distortion by anchoring validation to map-defined component identities and spatial constraints rather than unconstrained pixel-level comparison. This architecture reduces false positive and false negative determinations arising from global image variation.

[0078] The disclosed architecture thus differs structurally from inspection approaches that rely primarily on unconstrained image comparison, as it integrates:

[0079] a structured digital representation of component identities and relationships,

[0080] component-level detection.

[0081] alignment relative to map-defined expectations,

[0082] enforced component-wise correspondence, and

[0083] aggregation of component-specific validation outcomes.

[0084] The structured digital map provides a defined reference framework that governs detection, alignment, correspondence, validation, and aggregation operations described herein.

[0085] In certain embodiments, the enforcement of component-wise correspondence using the structured digital map and the computation of the geometric transformation provide a technical improvement in image-based inspection systems. The geometric transformation operates on image-derived spatial coordinate data corresponding to detected component locations and generates transformed coordinate representations stored in memory. By aligning detected component locations to expected component locations defined within a map-defined coordinate system prior to computing component-specific validation metrics, the system reduces sensitivity to lighting variation, pose variation, partial occlusion, and global image distortion. This coordinate-anchored validation architecture improves robustness and computational stability of component-level inspection operations compared to unconstrained global image comparison techniques. The resulting structured part-level validation result is therefore generated through a sequence of image processing and coordinate transformation operations thatproduce modified image-derived data representations within a computing system. A technical contribution resides in the coordinate-space transformation and enforced component-level correspondence that structurally constrain subsequent validation computations within the computing system.

[0086] 5.4 Structured Digital Map (FIG.3)

[0087] Referring now to FIG. 3, a schematic diagram illustrating an example structured digital map 300 of a reference part is shown, according to various example embodiments.

[0088] The structured digital map 300 corresponds to a reference part representation 302. The reference part representation 302 may correspond to a "golden” part, an ideal configuration, a defined reference instance, or another known reference configuration against w hich manufactured instances are validated.

[0089] The structured digital map 300 includes a plurality of component representations 304. Each component representation 304 corresponds to an individual component 306 of the reference part representation 302. Individual components 306 may correspond to physical features, subassemblies, fasteners, labels, text regions, logos, structural elements, or other distinguishable portions of the reference part representation 302.

[0090] Each individual component 306 may be associated with a component identifier 308. The component identifier 308 may uniquely identify the component 306 within the structured digital map 300 and may be used during validation to associate detected components with corresponding map-defined components.

[0091] The structured digital map 300 further encodes expected component locations 310 associated with individual components 306. Expected component locations 310 may define spatial positions relative to a coordinate system defined for the structured digital map 300.

[0092] In certain embodiments, the structured digital map 300 defines an explicit coordinate system, such as a two-dimensional or three-dimensional coordinate space. For example, as schematically illustrated in FIG. 3, the coordinate system may include an X-axis 320, a Y-axis 322, and an origin 324. In embodiments utilizing depth sensing, such as LiDAR or other three-dimensional imaging techniques, the coordinate system may further include a Z-axis (not shown) corresponding to depth values. Expected component locations 310 may be expressed as coordinate values within this map-defined coordinate system, including planar and / or depth coordinates.

[0093] In addition to individual expected component locations 310, the structured digital map 300 may encode expected spatial relationships 312 among the plurality of individual components 306. Expected spatial relationships 312 may include relative distances, orientations,alignments, adjacency relationships, proportional relationships, or other geometric relationships between components 306.

[0094] In certain embodiments, the structured digital map 300 may further encode one or more positional constraints 314. Positional constraints 314 may define allowable deviations, tolerances, or acceptable ranges of variation for individual components 306 relative to expected component locations 310 and / or expected spatial relationships 312.

[0095] The structured digital map 300 may also include reference imagery 316 associated with individual components 306. Reference imagery 316 may include stored image data representing an expected appearance of a corresponding component 306.

[0096] In various embodiments, the structured digital map 300 may further include reference textual or symbolic information 318 associated with individual components 306. Reference textual or symbolic information 318 may include expected text strings, symbols, markings, logos, alphanumeric identifiers, or other visual indicators associated with a component 306.

[0097] In certain embodiments, the structured digital map 300 may further encode non-visible imaging attributes associated with individual components 306. Such attributes may include thermal characteristics, emissivity ranges, reflectivity properties, ultraviolet (UV) response characteristics, fluorescence characteristics, material-dependent spectral signatures, or other sensor-derived properties. For example, in embodiments utilizing flash thermography, LiDAR, infrared imaging, UV imaging, or other non-visible sensing modalities, the structured digital map 300 may store reference thermal distributions, relative temperature gradients between components, depth information, or spectral response profiles associated with a reference part representation 302. These attributes may define acceptable ranges, tolerances, or comparative relationships used during validation of a manufactured instance.

[0098] Importantly, the structured digital map 300 is not limited to a static reference image, a flat template, or a simple computer-aided design (CAD) model. Rather, the structured digital map 300 encodes structured information including component identities 308, expected component locations 310, expected spatial relationships 312, positional constraints 314, reference imagery 316, and reference textual or symbolic information 318 in a manner that enables component-level correspondence and validation operations described herein.

[0099] In some embodiments, the structured digital map 300 may be generated based on image data of a reference manufactured part. In other embodiments, the structured digital map 300 may be generated using modeling tools, manual annotation, automated feature extraction, or combinations thereof. The structured digital map 300 may be stored in memory 230 of anelectronic device 200 and accessed by one or more processors 202 (FIG. 2) during validation operations.[000100] The structured digital map 300 thus provides a structured, component-aware reference framework that governs detection, alignment, correspondence enforcement, component-level validation, and aggregation operations described below.[000101] 5.5 Validation Process (FIG.4)[000102] Referring now to FIG. 4, a flowchart illustrating an example validation process 400 for validating a manufactured part is shown, according to various example embodiments.[000103] The validation process 400 may be executed by one or more processors 202 of an electronic device 200 (FIG. 2), which may form part of system 100 (FIG. 1).[000104] At step 402, the process 400 may include accessing a structured digital map 300 corresponding to a reference part representation 302. The structured digital map 300 may include component representations 304, individual components 306, component identifiers 308, expected component locations 310, expected spatial relationships 312, positional constraints 314, reference imagery 316, and reference textual or symbolic information 318, as described above with respect to FIG. 3.[000105] At step 404, the process 400 may include receiving image data representing a manufactured instance of the part. The image data may be captured using a client device 102-106 or received by a server 107-109 or application hosting server 113 via network 110 and / or 112.[000106] At step 406, the process 400 may include detecting a plurality of individual components within the received image data. Detection may identify image regions corresponding to components of the manufactured instance that correspond to component representations 304 and individual components 306 defined in the structured digital map 300.[000107] At step 408, the process 400 may include determining expected component locations 310 corresponding to the detected components based on the structured digital map 300.[000108] At step 410, the process 400 may include computing a geometric transformation to align detected component locations with expected component locations 310 defined by the structured digital map 300. The geometric transformation may align spatial relationships of detected components relative to expected spatial relationships 312 encoded in the structured digital map 300.[000109] At step 412, the process 400 may include establishing component- ise correspondence between detected components in the image data and individual components 306 represented in the structured digital map 300, including association with component identifiers 308.[000110] At step 414, the process 400 may include computing component-specific validation metrics for individual detected components based on comparisons between image data associated with the detected components and corresponding reference imagery 316 and / or reference textual or symbolic information 318 defined in the structured digital map 300.[000111] At step 416, the process 400 may include aggregating the component-specific validation metrics. Aggregation may consider positional constraints 314 and expected spatial relationships 312 when determining conformity.[000112] At step 418, the process 400 may include generating a structured part-level validation result comprising a conformity determination relative to the reference part representation 302.[000113] Although the steps 402-418 are illustrated in a particular order in FIG. 4, the disclosed process 400 is not limited to the specific sequence shown. In various embodiments, one or more of the steps 402-418 may be performed in a different order, may be performed in parallel, may be combined, or may be omitted, provided that component-wise correspondence relative to the structured digital map 300 is established prior to computation of componentspecific validation metrics and aggregation operations dependent on such correspondence.[000114] The flowchart of FIG. 4 is therefore illustrative and non-limiting.[000115] 5.6 Alignment and Component Correspondence (FIG.5)[000116] Referring now to FIG. 5, a schematic diagram illustrating an example alignment system 500 is shown, according to various example embodiments.[000117] The alignment system 500 operates to align detected component locations 504 derived from a captured image 502 of a manufactured instance with expected component locations 506 defined by the structured digital map 300 (FIG. 3).[000118] The captured image 502 may include a plurality of detected component locations 504 corresponding to components identified during detection step 406 of validation process 400 (FIG. 4). The structured digital map 300 defines expected component locations 310 (FIG. 3). which correspond to expected component locations 506 shown schematically in FIG. 5.[000119] A geometric transformation 508 is computed to align the detected component locations 504 with the expected component locations 506. In various embodiments, the geometric transformation 508 may be represented by a transformation matrix 510.[000120] The transformation matrix 510 may include parameters corresponding to one or more geometric operations, including translation, rotation, scaling, reflection, shear, affine transformation, or combinations thereof. In certain embodiments, the geometric transformation 508 may be modeled as an affine transformation that maps coordinates of detected componentlocations 504 into a coordinate space corresponding to expected component locations 506 defined by the structured digital map 300.[000121] The geometric transformation 508 may be computed to reduce or minimize spatial error between detected component locations 504 and corresponding expected component locations 506. For example, transformation parameters of transformation matrix 510 may be determined such that a cumulative error metric representing deviation between detected component locations 504 and expected component locations 506 is reduced according to a selected optimization criterion.[000122] In certain embodiments, as schematically represented in FIG. 5, the geometric transformation 508 may be determined by minimizing a cumulative spatial error metric 514 computed between transformed detected component locations 504 and expected component locations 506 defined by the structured digital map 300.[000123] The alignment performed by geometric transformation 508 is computed relative to the structured digital map 300, as opposed to relying solely on unconstrained image-to-image alignment techniques. By aligning detected component locations 504 directly to expected component locations 506 defined by the structured digital map 300, the system enforces component-wise correspondence.[000124] The geometric transformation and correspondence mapping are performed on image-derived spatial coordinate data and produce transformed coordinate representations stored in memory and used to control subsequent component-level validation operations.[000125] Following application of geometric transformation 508, a component correspondence mapping 512 may be established. The component correspondence mapping 512 associates each detected component location 504 with a specific individual component 306 (FIG.3) identified by a component identifier 308 defined within the structured digital map 300.[000126] The enforcement of component-wise correspondence via transformation 508 and mapping 512 enables subsequent computation of component-specific validation metrics 414 (FIG. 4) to be performed relative to defined component representations 304, reference imagery 316, reference textual or symbolic information 318, expected spatial relationships 312, and positional constraints 314 encoded within the structured digital map 300.[000127] The alignment system 500 therefore differs structurally from global image registration approaches that align whole images without establishing explicit correspondence between detected components and map-defined component identities. In the disclosed architecture, geometric transformation 508 and component correspondence mapping 512 operate in conjunction with structured digital map 300 to enforce alignment at the level of defined components.[000128] The schematic representation of FIG. 5 is illustrative and non-limiting, and alternative transformation models and optimization techniques may be employed without departing from the scope of the present disclosure.[000129] 5.7 Component-Level Validation and Aggregation (FIGS.4 and 6)[000130] Following establishment of component-wise correspondence at step 412 of validation process 400 (FIG. 4), component-specific validation metrics may be computed at step 414.[000131] For each detected component location 504 (FIG. 5) associated with a corresponding individual component 306 (FIG. 3) via component correspondence mapping 512 (FIG. 5), one or more component-specific validation metrics may be determined.[000132] Component-specific validation metrics may be computed based on comparisons between image data associated with a detected component and reference information encoded in the structured digital map 300.[000133] For example:[000134] Image data associated with a detected component may be compared to reference imagery' 316 using template matching, feature comparison, similarity scoring, or other image comparison techniques.[000135] Textual or symbolic content extracted from image data associated with a detected component may be compared to reference textual or symbolic information 318.[000136] Optical character recognition (OCR) techniques may be applied to image data associated with a detected component to extract text for comparison with reference textual or symbolic information 318.[000137] Spatial deviation relative to expected component location 310 and / or expected spatial relationships 312 may be evaluated in view of positional constraints 314.[000138] In certain embodiments, component-specific validation may be performed using non-visible imaging data captured from the manufactured instance. For example, thermal image data, infrared data, ultraviolet (UV) image data, structured light data, LiDAR-derived depth data, or other sensor-derived data may be compared against corresponding reference attributes encoded in the structured digital map 300. In embodiments utilizing flash thermography, a transient thermal excitation may be applied to the manufactured instance and resulting temperature distributions may be captured and compared to reference thermal characteristics stored in the structured digital map 300. Validation may include comparing relative temperature values among components, detecting deviations indicative of alternate materials, structural defects, or stress fractures, or identify ing outlier thermal responses relative to defined acceptable ranges.[000139] Each detected component may therefore be assigned a component-specific validation metric reflecting conformity, deviation magnitude, confidence score, similarity score, error measurement, or other quantitative or qualitative indicator.[000140] At step 416 of validation process 400, the component-specific validation metrics may be aggregated to generate an aggregate assessment.[000141] Aggregation may be performed in a variety of ways. In some embodiments, aggregation may include computing an unweighted aggregate score based on the componentspecific validation metrics. In other embodiments, aggregation may include computing a weighted aggregate score in which different individual components 306 are assigned different relative importance values. Weighting may reflect, for example, functional significance, regulatory requirements, safety relevance, or other considerations associated with specific components.[000142] In further embodiments, aggregation may include rule-based logic, thresholdbased logic, voting mechanisms. Boolean combinations, hierarchical aggregation, or combinations thereof. The disclosed architecture is not limited to a particular aggregation methodology7.[000143] At step 418 of validation process 400, a structured part-level validation result may be generated. The structured part-level validation result may comprise a conformity determination relative to the reference part representation 302.[000144] In certain embodiments, conformity or non-conformity may be determined based on comparison of an aggregate score to a predetermined threshold. In other embodiments, nonconformity may be determined if one or more critical components fail to satisfy7positional constraints 314 or validation criteria derived from reference imagery 316 or reference textual or symbolic information 318.[000145] The structured part-level validation result may include identification of one or more specific individual components 306 determined to be non-conforming.[000146] Referring now to FIG. 6, a schematic diagram illustrating an example validation metric computation and conformity determination system 600 is shown, according to various example embodiments. The system 600 may operate on a structured part-level validation result 602 generated as described above and may compute, aggregate, and evaluate component-level validation metrics to determine conformity relative to a reference part.[000147] In various embodiments, the system 600 may include a validation metric computation engine 604 configured to perform component-level error aggregation 606 and spatial deviation quantification 608 to generate computed validation metrics 612. The system 600 may further include conformity threshold criteria 610 and a conformity determinationengine 614 configured to apply the threshold criteria to the computed validation metrics 612 in order to generate one or more conformity classifications 616 and a structured conformity result 618. In certain embodiments, the conformity classifications may comprise binary pass / fail determinations. In other embodiments, the conformity classifications may include multiple levels of conformity, such as pass, warning, fail, or other graduated states indicative of proximity to one or more thresholds. The aggregation and threshold evaluation mechanisms illustrated in FIG. 6 are exemplary and non-limiting, and alternative metric computation, weighting, aggregation, classification, or scoring techniques may be employed.[000148] In some embodiments, the structured conformity result 618 may be presented to a user via a validation output interface that includes graphical indicators identifying one or more non-conforming components within associated image data. Such graphical indicators may include visual overlays, highlighting, bounding regions, color-coded indicators, deviation magnitude indicators, component-level scores, and / or aggregate validation scores. The manner of presenting validation results may vary and is not limited to a particular visualization format.[000149] 5.8 Validation Output Interface (FIG.7)[000150] Referring now to FIG. 7, a schematic diagram illustrating an example validation output interface 700 is shown, according to various example embodiments. The validation output interface 700 may present validation results derived from the conformity determination system 600 (FIG. 6) in a user-accessible format. In various embodiments, the validation output interface 700 may receive the structured conformity result 618 and associated component-level validation metrics 612 generated by system 600 and display component-level and aggregate validation information.[000151] The validation output interface 700 may include a visual representation of a manufactured part 702. The visual representation 702 may correspond to image data captured during the validation process (e.g., step 404 of FIG. 4), a processed version of the image data, or a rendered representation derived from structured digital map 300. The visual representation 702 may display individual components of the manufactured instance.[000152] In various embodiments, one or more non-conforming components 704 may be identified within the visual representation 702. Non-conforming components 704 may correspond to individual components 306 (FIG. 3) for which component-specific validation metrics 612 indicate deviation beyond acceptable thresholds defined by conformity threshold criteria 610 (FIG. 6).[000153] The validation output interface 700 may include overlay indicators 706 configured to visually mark non-conforming components 704 within the visual representation 702. Overlay indicators 706 may include highlighting, bounding boxes, outlines, graphicalmarkers, shading, color-coding, annotations, or other visual demarcations applied to the image data. In certain embodiments, overlay indicators 706 may be dynamically generated based on the structured conformity' result 618.[000154] In some embodiments, the validation output interface 700 may provide a user-selectable display of a ''golden7’ image or reference representation corresponding to a detected component. The so-called golden image (which is typically not actually golden in color) may represent an expected appearance of the component as defined by the structured digital map 300. In certain embodiments, the golden image may be geometrically transformed — including via translation, rotation, scaling, affine transformation, or other coordinate-space operations — to align with the detected component location within the visual representation 702. Such transformation may allow the golden image to be displayed in registration with the evaluated component, including in an overlaid configuration or side-by-side comparison, thereby enabling visual assessment of conformity, deviation, or proximity to threshold classifications.[000155] In some embodiments, deviation magnitude indicators 708 may be displayed in association with non-conforming components 704. Deviation magnitude indicators 708 may represent a degree of deviation associated with a component-specific validation metric, including spatial deviation, image similarity deviation, textual mismatch, symbolic inconsistency, or other quantified measures. Deviation magnitude indicators 708 may include numerical values, graphical bars, graded symbols, color-coded indicators, confidence scores, or other representations of deviation magnitude.[000156] The validation output interface 700 may further display component-specific scores 710 corresponding to individual detected components. Component-specific scores 710 may reflect values derived from component-specific validation metrics 612 and may indicate conformity, deviation magnitude, similarity score, confidence score, or other evaluation outputs.[000157] In certain embodiments, the validation output interface 700 may display an aggregate validation score 712 derived from aggregation of component-specific validation metrics 612. The aggregate validation score 712 may correspond to the structured conformity result 618 and may indicate overall conformity or non-conformity of the manufactured instance relative to the reference part.[000158] The validation output interface 700 is illustrative and non-limiting. The arrangement, format, and presentation of overlay indicators 706, deviation magnitude indicators 708, component-specific scores 710, and aggregate validation score 712 may vary. The interface 700 may be implemented on a client device 102-106, a server-rendered web interface, a mobile application, a desktop application, an augmented reality device, or other computing platform.[000159] In certain embodiments, the validation output interface 700 may provide explanatory information indicating reasons for non-conformity, including deviations from expected component locations 310, expected spatial relationships 312, positional constraints 314, reference imagery 316, or reference textual or symbolic information 318 encoded in the structured digital map 300.[000160] 5.9 Variations and Alternative Embodiments[000161] The embodiments described herein are illustrative and non-limiting. Various modifications, extensions, and alternative configurations may be implemented while maintaining the component-aware, map-driven validation architecture described above.[000162] 5.10 Distributed and Hybrid Processing[000163] In certain embodiments, detection, alignment, correspondence, validation, and aggregation operations may be performed on a client device 102-106. In other embodiments, one or more of these operations may be performed on a sen- er 107-109 or application hosting server 113. In further embodiments, processing may be distnbuted between client devices and servers, including edge computing configurations in which component detection is performed locally and alignment, aggregation, or reporting is performed remotely.[000164] The structured digital map 300 may be stored locally in memory 230 of an electronic device 200 or remotely in a server environment. In some embodiments, portions of structured digital map 300 may be cached locally to facilitate real-time validation.[000165] 5.11 Real-Time and Batch Validation[000166] Validation process 400 may be performed in real time as image data is captured by a client device 102-106. Alternatively, image data may be stored and processed in batch form. In certain embodiments, validation process 400 may be integrated into manufacturing workflows, inspection stations, robotic systems, or quality' control pipelines.[000167] 5.12 Hierarchical and Multi-Level Component Modeling[000168] In some embodiments, component representations 304 may be organized hierarchically. An individual component 306 may include subcomponents, each having associated component identifiers 308, expected component locations 310, expected spatial relationships 312, and positional constraints 314. Component-specific validation metrics may be computed at multiple levels of hierarchy and aggregated using hierarchical aggregation strategies.[000169] 5.13 Adaptive or Updated Digital Maps[000170] In certain embodiments, structured digital map 300 may be updated over time. For example:[000171] Reference imagery 316 may be refined based on additional reference data.[000172] Positional constraints 314 may be adjusted based on observed manufacturing tolerances.[000173] Expected spatial relationships 312 may be recalibrated.[000174] Component identifiers 308 may be expanded to accommodate new component types.[000175] Updates to structured digital map 300 may be performed manually, automatically, or semi-automatically.[000176] 5.14 Alternative Transformation Models[000177] Although geometric transformation 508 may be represented by transformation matrix 510 and may include affine transformations, other transformation models may be used. For example, non-linear transformations, perspective transformations, projective transformations, or locally adaptive transformations may be employed to align detected component locations 504 with expected component locations 506.[000178] Optimization criteria for computing geometric transformation 508 may van-. Spatial error minimization may be performed using least-squares methods, robust estimators, weighted error metrics, or other optimization techniques.[000179] 5.15 Alternative Validation Metrics[000180] Component-specific validation metrics may include:[000181] Image similarity scores,[000182] Feature vector comparisons,[000183] Machine learning classifier outputs,[000184] Confidence scores,[000185] Binary pass / fail determinations,[000186] Multi-dimensional metric vectors.[000187] Validation may incorporate one or more machine learning models trained to evaluate conformity based on component imagery and structured map features.[000188] 5.16 Multi-Part and Comparative Validation[000189] In certain embodiments, validation may be performed across multiple manufactured instances simultaneously. Structured digital map 300 may be used to compare multiple instances to a common reference part representation 302. In further embodiments, structured digital maps corresponding to different reference configurations may be selected dynamically based on part type or classification.[000190] In further embodiments, validation of a manufactured instance may be performed using multiple images of the same part. The multiple images may comprise a series of photographs captured sequentially, frames extracted from a video stream, or image data capturedfrom multiple viewpoints. In such embodiments, the component detection, geometric transformation, correspondence mapping, and component-specific validation metrics described herein may be applied to each image independently, and the resulting validation metrics may be aggregated across images to generate a consolidated conformity determination. Aggregation across images may improve robustness to occlusion, lighting variation, pose variation, motion blur, or transient artifacts, and may include averaging, weighted scoring, confidence accumulation, majority voting, temporal smoothing, or other multi-frame evaluation techniques.[000191] 5.17 Explainability and Reporting[000192] The structured nature of structured digital map 300 and component-wise correspondence mapping 512 enables explainable validation outputs. In some embodiments, a validation output interface 700 may provide detailed explanations indicating which individual components 306 failed validation and why, based on deviations from expected component locations 310, expected spatial relationships 312, positional constraints 314, reference imagery 316, or reference textual or symbolic information 318. The validation output interface 700 may receive the structured conformity result 618 generated by the conformity determination system 600 and present component-level and aggregate validation information in a user-accessible format.[000193] 5.18 Security and Authentication Applications[000194] In some embodiments, the disclosed component-aware validation architecture may be applied to authentication or anti-counterfeiting scenarios in which conformity to reference part representation 302 indicates authenticity.[000195] 5.19 Additional Non-Limiting Systems and Methods[000196] In addition to the component- aware, map-driven validation techniques described above, the present disclosure (including the provisional application to which priority is claimed) further describes additional computer-implemented systems and methods. These systems and methods may be implemented independently, or in any suitable combination with one another and / or with the intelligent part identification and validation embodiments described herein.[000197] Unless expressly stated otherwise, processes described in this section may be performed in various orders, may omit one or more steps, may include additional steps, and / or may be combined with other processes or steps described herein. The embodiments described below are illustrative and non-limiting.[000198] 5.19.1 Training Image Generation for Object Detection Models [000199] In certain embodiments, the present disclosure provides systems and methods for generating labeled image data for training one or more machine learning or artificial intelligence models, including object detection models.[000200] In some implementations, a plurality of photographs of an object may be obtained. A three-dimensional model of the object may be generated based on the photographs. The three-dimensional model may be imported into a three-dimensional environment or simulation environment.[000201] Within the three-dimensional environment, object features may be labeled. A plurality of images may then be generated from the three-dimensional model under varied conditions. Such variations may include differences in orientation, rotation, scale, lighting conditions, shadows, background environments, occlusion effects, perspective variations, and other environmental factors.[000202] Generated images may be automatically annotated to produce labeled image data. Annotation may include bounding boxes, segmentation masks, keypoints, or other feature indicators corresponding to object features. The labeled images may be used to train an object detection model, classification model, segmentation model, or other machine learning model.[000203] In certain embodiments, synthetic image generation may supplement or replace manually collected labeled datasets. The trained model may thereafter be deployed in a production environment to detect objects or components within captured image data.[000204] In some embodiments, the training image generation techniques described in this subsection are additional aspects that may be used in connection with the intelligent part identification and validation embodiments described herein, including to support training of an object detection model applied during component detection (e.g., step 406 of process 400).[000205] 5.19.2 Specification Extraction Using OCR and Proprietary Rules [000206] In certain embodiments, the present disclosure provides systems and methods for extracting specification information from photographs using optical character recognition (OCR) and rule-based classification techniques.[000207] A photograph may be received, such as a photograph of a nameplate, label, equipment plate, or other textual region associated with a manufactured item. The photograph may be preprocessed to enhance text visibility, including operations such as cropping, filtering, thresholding, contrast adjustment, geometric correction, or other preprocessing techniques.[000208] OCR may be performed on the preprocessed image to extract textual data.Extracted text may be grouped into chunks or candidate fields. The chunks may be evaluated using a collection of rules to determine whether they correspond to specification markers.[000209] In certain embodiments, extracted values may include Manufacturer, Make, Model number, Serial number, or other specification values (sometimes referred to collectively as MMS values). Each extracted chunk may be assigned to one or more candidate buckets associated with particular specification markers. Scores may be computed for each bucket basedon rule evaluation, patern matching, contextual information, string similarity, position information, or other criteria.[000210] When a bucket score exceeds a threshold, the corresponding chunk may be classified as representing the associated specification marker. In some embodiments, multiple candidate values may be evaluated and ranked.[000211] The extracted and classified specification information may be returned to a user device, stored in a database, or used as input to other systems or workflows.[000212] In some embodiments, the OCR-based specification extraction techniques described in this subsection are additional aspects that may be used in connection with the intelligent part identification and validation embodiments described herein, including to support extraction and validation of textual or symbolic features (e.g., reference textual or symbolic information 318) associated with one or more components.[000213] 5.19.3 Equipment Library Searching Using MMS and Intelligent Part Identification[000214] In certain embodiments, the present disclosure provides systems and methods for searching an equipment library or database using multiple forms of identification data.[000215] An equipment library may include semantic data, specifications, manuals, nameplate data, technical documentation, images, or other records associated with equipment or manufactured items. The equipment library may be implemented using one or more databases accessible via system 100.[000216] In some embodiments, search inputs may include specification information such as Manufacturer, Make, Model number, Serial number (MMS values), image-based identification outputs, or combinations thereof.[000217] Search operations may be performed based on MMS values extracted as described above, image-based intelligent part identification results, or both. In certain embodiments, search results obtained using MMS values may be cross-referenced with search results obtained using intelligent part identification techniques. The results may be filtered, ranked, merged, or otherwise combined to produce a combined list of candidate results.[000218] The combined results may be returned to a mobile device, web client, or other user interface for presentation to a user. In some embodiments, validation results and search results may be displayed together.[000219] In some embodiments, the equipment library searching techniques described in this subsection are additional aspects that may be used in connection with the intelligent part identification and validation embodiments described herein, including to consume one or morevalidation outputs (e.g., a structured part-level validation result) as inputs to a database search and cross-referencing process for presenting search results to a user.[000220] 5.20 Conclusion of Detailed Description[000221] The foregoing detailed description has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise forms described. Numerous modifications, variations, combinations, and alternative embodiments will be apparent to those skilled in the art in view of the teachings herein.[000222] Unless expressly stated otherwise, no element, step, component, or function described herein should be interpreted as required in all embodiments. Individual elements and features described in connection with particular embodiments may be combined with elements and features of other embodiments without departing from the scope of the present disclosure.[000223] The order of operations described herein is illustrative and non-limiting.Operations may be performed in different sequences, in parallel, in combination, or may be omitted, provided that the resulting system or method maintains functional operability consistent with the teachings herein.[000224] Reference to a singular element includes the plural unless explicitly stated otherwise. Likewise, reference to a plurality may include a singular embodiment where context permits.[000225] The structured digital map 300, validation process 400, alignment system 500, validation metric and conformity' determination system 600, validation output interface 700, and additional systems and methods described in Section 5.9 may be implemented using hardware, software, firmware, or any suitable combination thereof. The present disclosure encompasses implementations in distributed computing environments, cloud-based systems, edge computing environments, and standalone computing devices.[000226] No statement in this detailed description is intended to constitute an admission of prior art. Any discussion of existing approaches is provided solely for contextual explanation and should not be construed as an acknowledgment that such approaches are prior art to the claimed subj ect matter.[000227] The scope of protection is defined by the claims that follow, including all equivalents to which those claims are entitled.[000228] The term ‘comprising’ does not exclude other elements or steps, and the indefinite article ‘a’ or ‘an’ does not exclude a plurality.

Claims

1. What is claimed is:

1. A computer-implemented method for validating a manufactured part, the method comprising:accessing, by one or more processors, a structured digital map of a reference part, the structured digital map encoding:(i) representations of a plurality of individual components of the reference part, and(ii) expected spatial relationships among the plurality of individual components;receiving image data representing a manufactured instance of the part;detecting, from the image data, a plurality of individual components of the manufactured instance;computing a geometric transformation aligning locations of the detected individual components with expected component locations defined by the structured digital map, thereby establishing an association between each detected component and a corresponding component representation encoded in the structured digital map;computing, for individual ones of the detected components, respective component-specific validation metrics each comprising a value indicative of conformity or deviation relative to the structured digital map based on comparisons between image data associated with the detected components and corresponding reference information defined by the structured digital map; and aggregating the component-specific validation metrics to generate a structured part-level validation result comprising a conformity determination relative to the reference part.

2. A system for validating a manufactured part, comprising:one or more processors; andone or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to:access a structured digital map of a reference part, the structured digital map encoding:(i) representations of a plurality of individual components of the reference part, and(ii) expected spatial relationships among the plurality of individual components;receive image data representing a manufactured instance of the part;detect, from the image data, a plurality’ of individual components of the manufactured instance; determine, based on the structured digital map, expected component locations corresponding to the plurality of individual components;compute a geometric transformation that aligns locations of the detected individual components with the expected component locations defined by the structured digital map, thereby establishing an association between each detected component of the manufactured instance and a corresponding component representation encoded in the structured digital map. the geometrictransformation being computed using coordinate information defined by the structured digital map as a reference framework;compute, for individual ones of the detected components, respective component-specific validation metrics each comprising a value indicative of conformity or deviation relative to the structured digital map based on comparisons between image data associated with each detected component and corresponding reference information defined by the structured digital map; and aggregate the component-specific validation metrics to generate a structured part-level validation result comprising a conformity determination relative to the reference part.

3. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: accessing, by one or more processors, a structured digital map of a reference part, the structured digital map encoding:(i) representations of a plurality of individual components of the reference part, and(ii) expected spatial relationships among the plurality of individual components;receiving image data representing a manufactured instance of the part;detecting, from the image data, a plurality of individual components of the manufactured instance;computing a geometric transformation aligning locations of the detected individual components with expected component locations defined by the structured digital map, thereby establishing an association between each detected component and a corresponding component representation encoded in the structured digital map;computing, for individual ones of the detected components, respective component-specific validation metrics each comprising a value indicative of conformity’ or deviation relative to the structured digital map based on comparisons between image data associated with the detected components and corresponding reference information defined by the structured digital map; and aggregating the component-specific validation metrics to generate a structured part-level validation result comprising a conformity determination relative to the reference part.

4. The method of claim 1, wherein computing the geometric transformation comprises computing a transformation matrix mapping detected component locations to expected component locations defined by the structured digital map.

5. The method of claim 4, wherein the transformation matrix minimizes spatial error between detected component locations and expected component locations.

6. The method of claim 1, wherein the geometric transformation comprises at least one of translation, rotation, scaling, or affine transformation.

7. The method of claim 1, wherein detecting the plurality of individual components comprises applying a trained object detection model to the image data.

8. The method of claim 1, wherein detecting the plurality of individual components comprises identifying component regions within the image data and extracting image data corresponding to each identified component region.

9. The method of claim 1, wherein computing the component-specific validation metrics comprises performing template matching between image data associated with each detected component and corresponding reference imagery' defined by the structured digital map.

10. The method of claim 1, wherein computing the component-specific validation metrics comprises extracting textual or symbolic features from image data associated with a detected component and comparing the extracted textual or symbolic features with reference textual or symbolic information defined by the structured digital map.

11. The method of claim 10, wherein extracting textual features comprises performing optical character recognition on image data associated with the detected component.

12. The method of claim 1, wherein the structured digital map encodes positional constraints defining allowable deviations for individual components relative to expected spatial relationships.

13. The method of claim 1, wherein establishing the association between each detected component and the corresponding component representation comprises associating each detected component with a specific component identifier defined in the structured digital map.

14. The method of claim 1 , wherein aggregating the component-specific validation metrics comprises computing a weighted aggregate score based on weighting values assigned to individual components to reflect differing conformity significance.

15. The method of claim 1, wherein generating the structured part-level validation result comprises determining conformity or non-conformity based on comparison of an aggregate score to a predetermined threshold.

16. The method of claim 1, wherein the structured part-level validation result includes identification of one or more specific components determined to be non-conforming.

17. The method of claim 1, further comprising generating a visual overlay identifying one or more non-conforming components within the image data.

18. The method of claim 17, wherein the visual overlay includes graphical indicators corresponding to deviation magnitude.

19. The method of claim 1 , wherein the image data is captured using a camera-equipped mobile device.

20. The method of claim 1, wherein the structured digital map is generated based on image data of a reference manufactured part.

21. The method of claim 1, wherein computing the geometric transformation comprises minimizing a cumulative spatial error metric between detected component locations and expected component locations defined by the structured digital map.

22. The method of claim 1 , wherein the structured digital map defines a coordinate system within which expected component locations are expressed.

23. The method of claim 1 , wherein computing the geometric transformation comprises determining parameters of a transformation matrix by minimizing a least-squares error between transformed detected component locations and expected component locations defined by the structured digital map.

24. The method of claim 1 , wherein the conformity7determination is generated based on evaluation of spatial deviation of one or more detected components relative to positional constraints encoded in the structured digital map.

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