Automated Vehicle Inspection (AVI)

• Automated Vehicle Inspection (AVI) is a systems category that uses multi-modal sensing and AI to automate the inspection of vehicles, roadways, and infrastructure.
• It integrates techniques like detection, segmentation, and geometric inference across platforms—from dashcams to UAVs—to replace manual, costly inspections.
• AVI enhances operational efficiency by linking real-time sensing with end-to-end decision support, optimizing both inspection accuracy and vehicle configuration.

Searching arXiv for the cited AVI-related papers to ground the article. Automated Vehicle Inspection (AVI) denotes a class of automated inspection workflows in which vehicles are either the inspection target, the sensing platform, or both. In the arXiv literature, the term spans several operational regimes: camera-based road infrastructure assessment from vehicle-captured imagery, AI-based road maintenance inspection from dashcam data, roadside monitoring of moving, idling, and engine-off vehicle states, image-based vehicle classification for electronic toll collection, drive-by bridge health monitoring with an instrumented inspection vehicle, and factory-line multi-view quality control of finished vehicles; related UAV-based work extends the same automation logic to buildings, tunnels, and power transmission lines (Chen et al., 2021, Mukherjee et al., 2021, Li et al., 2024, Ng et al., 2012, Hurtado et al., 3 Oct 2025, Kulkarni et al., 30 Sep 2025, Srivastava et al., 2022, Pahwa et al., 2019, Nekovář et al., 2023). The shared objective is to replace slow, subjective, dangerous, or expensive manual inspection with sensing, inference, geometric reasoning, and decision support.

1. Scope, terminology, and historical development

The literature does not use AVI in a single narrow sense. In road-infrastructure work, AVI refers to automating visual inspection from vehicle-captured imagery so that road defects and markings can be detected, validated, stored, reviewed, and exported for operational use by local governments (Chen et al., 2021). In roadside monitoring, AVI is framed as automated inspection of a vehicle’s operational state, specifically whether it is moving, idling, or engine off (Li et al., 2024). In electronic toll collection and multi-lane free flow, an early vision-based formulation appears as automatic vehicle detection and classification, where classification is required so that tolls are charged according to vehicle classes (Ng et al., 2012). In automotive manufacturing, AVI denotes end-to-end, multi-view, variant-aware quality control that verifies whether a vehicle matches a VIN-derived manifest and is free of visible defects (Kulkarni et al., 30 Sep 2025).

A common misconception is that AVI refers only to inspection of vehicles themselves. The surveyed work shows a broader pattern: the inspection task may concern road surfaces, road markings, barriers, traffic signs, bridges, buildings, tunnels, transmission lines, or manufactured vehicles, provided that the workflow is automated and vehicle-mediated in acquisition or execution. This suggests that AVI is better understood as an operational systems category than as a single application domain.

Historically, the progression runs from relatively compact feature-engineering pipelines toward integrated, multi-module systems. The 2012 image-based vehicle classification system used grayscale conversion, segmentation, modified SIFT descriptors, Canny edge detection, K-means clustering, and Euclidean distance matching for inter-class car/van and intra-class sedan/taxi discrimination on 530 segmented vehicle images (Ng et al., 2012). By 2021, road-inspection systems had become multi-task platforms combining detection, segmentation, dashboards, databases, APIs, and geolocated map views (Chen et al., 2021, Mukherjee et al., 2021). More recent work introduces multimodal fusion for idling detection, semantic rule engines for variant-aware factory inspection, and optimization of the inspection vehicle itself for bridge health monitoring (Li et al., 2024, Kulkarni et al., 30 Sep 2025, Hurtado et al., 3 Oct 2025).

2. Sensing platforms and data acquisition regimes

AVI systems differ first at the acquisition layer. RoadAtlas is built around road imagery collected from a vehicle and was trained from 10,000 raw road images captured in Logan City, Australia, initially unlabelled and later annotated by five annotators with irregular polygons for background, Kerb_Cracking, Road_Crocodile, Road_Longitudinal, Road_Transverse, Road_Block, and Sealed_Crack (Chen et al., 2021). The AI-driven road maintenance pipeline likewise starts from vehicle-mounted camera imagery, illustrated with dashcam examples, and then interpolates GPS coordinates for each video frame so that findings can be pinned on a map (Mukherjee et al., 2021). The broader road-inspection survey distinguishes 2-D camera systems from 3-D systems such as laser scanning, Kinect/depth sensing, multi-view geometry, stereo vision, and time-of-flight approaches, emphasizing that defect geometry strongly influences sensor suitability (Fan et al., 2022).

Roadside AVI for idling detection uses a different sensing arrangement: a surveillance-style webcam mounted about 20 feet above the ground and six roadside microphones. The AVIVD dataset was collected over four days, four hours per day, and sampled into one-second synchronized video-audio clips. The training split contains 76,940 clips and the validation split 8,431, with annotations for moving, idling, and engine-off vehicles (Li et al., 2024). This is not a defect-inspection setup but an operational-state inspection setup.

Manufacturing AVI adopts fixed, synchronized multi-view rigs. The production-line platform uses 11 synchronized cameras with resolution $3840 \times 2160$, triggered by a light-beam sensor to obtain a full $360^\circ$ sweep of each vehicle. Task-specific views are assigned to frontal branding cues, top-down roof features, rear features, wheel-type views, and side-facing scratch-and-dent views (Kulkarni et al., 30 Sep 2025). This is a high-throughput, controlled-environment acquisition regime, unlike roadside or field inspection.

Structural and infrastructure inspection work broadens the concept of inspection vehicle further. Drive-by bridge inspection uses a purpose-built inspection vehicle that traverses a bridge at constant speed while recording acceleration responses for indirect structural health monitoring (Hurtado et al., 3 Oct 2025). UAV-based building inspection uses a DJI Mavic Mini with frontal, in-between, and roof acquisition modes, plus roof videos sampled at 10 Hz and 1 Hz for stitching-based layout estimation (Srivastava et al., 2022). Tunnel inspection uses a lightweight GoPro HERO4 mounted on a rotating shaft, capturing one image every $36^\circ$, yielding 10 images per $360^\circ$ rotation and approximately 7,500 pixels per full stitched sweep (Pahwa et al., 2019). Power transmission line inspection assumes autonomous multicopters with battery-limited missions, maximum travel velocity $v_t = \SI{5}{\meter\per\second}$, and maximum inspection velocity $v_i = \SI{1}{\meter\per\second}$ (Nekovář et al., 2023).

3. Perception, segmentation, and geometric inference

Most AVI systems are multi-stage rather than monolithic. RoadAtlas uses a two-stage crack-recognition pipeline consisting of a crack detector inspired by ultralytics/yolov5 v3.1 and a crack segmenter based on U-Net. Before recognition, the system applies anonymization with the understand.ai Anonymizer and converts annotated polygons into minimized bounding boxes. The marking subsystem follows PSPNet and then refines outputs through ROI detection, projection from street view into bird eye view, and low-level contour comparison before mapping contours back onto the original image (Chen et al., 2021).

The AI-driven road maintenance inspection pipeline distributes tasks across object detection, semantic segmentation, and rule-based or geometric post-processing. Road damage detection uses SSD with HarDNet as backbone, specifically HarDNet85 for road damage detection, on grouped super-categories including alligator cracks, transverse cracks, longitudinal cracks, missing markings, and potholes. Road marking damage is treated as a two-stage problem: RGPNet first segments marking regions, and the masks are then refined by adaptive pixel intensity thresholding, a bank of differential filters, and SLIC superpixels to estimate damaged area. Traffic sign skew is inferred geometrically from a segmented pole via minimum rotated rectangle analysis, and barrier safety is inferred from contours, convex hulls, and solidity thresholds (Mukherjee et al., 2021).

The roadside idling system is explicitly multimodal because no single modality suffices: moving vehicles have motion and sound, idling vehicles are stationary but still emit engine sound, and engine-off vehicles are stationary and silent. AVIVD-Net therefore combines a 3D CNN on video, a 2D CNN on synchronized audio, a bidirectional audio-visual attention mechanism, and a YOWO-style region proposal network. The total objective is reported as

$$l_{total}=l_{Focal}+l_{x}+l_{y}+l_{w}+l_{h}+l_{conf},$$

which aligns the task with joint detection, localization, and state classification (Li et al., 2024).

Production-line AVI is even more explicitly modular. YOLOv8 is used for part detection, EfficientNet for ICE/EV classification from the front grille crop, Gemini-1.5 Flash for mascot OCR, and YOLOv8-Seg for scratch-and-dent segmentation. View-aware evidence fusion is performed by max pooling,

$$S_t = \max_{v \in V_t, (b_i, s_i) \in D_{t,v}} s_i,$$

followed by VIN-conditioned rule checking through expected, missing, and extra feature sets. A vehicle passes only if no expected features are missing, no extra features are present, and no damage is detected (Kulkarni et al., 30 Sep 2025).

Geometric AVI pipelines often combine perception with reconstruction. UAV-based building inspection uses panoptic segmentation for vegetation removal, image-based 3D reconstruction with SfM/MVS, Euclidean clustering, RANSAC plane fitting, LEDNet roof segmentation, ORB features with optical flow and RANSAC affine estimation for roof stitching, and Detic for rooftop non-structural elements (Srivastava et al., 2022). Tunnel inspection uses SIFT, SURF, or ORB for local features, RepMatch for wide-baseline matching in repetitive geometry, bundle adjustment with geometry-aware pruning, dense reconstruction, texture mapping, and Unity-based visualization (Pahwa et al., 2019). The road-inspection survey places these model families into a larger taxonomy spanning traditional image analysis, CNN-based classification and segmentation, disparity transformation, quadratic surface fitting, and hybrid 2-D/3-D pipelines (Fan et al., 2022).

4. End-to-end system architectures and decision workflows

AVI becomes operational when perception is embedded in data management, human validation, and reporting. RoadAtlas is explicitly organized into three main modules—dashboard, deep visual models, and backend—and supports road defect detection, road marking parsing, a web-based dashboard for presenting and inputting data, and a backend with a structured database and APIs. The backend is implemented with Python Flask, stores data in MongoDB, and provides 20 TB of hard disk storage. The dashboard exposes four functions: Road Defect, Road Marking, Database Update, and Report Export. The defect view presents an interactive map with pins, labelled images, coordinates, and metadata, allowing users to validate correctness and mark defects as checked; validated data can be exported as CSV or JSON (Chen et al., 2021).

The AI-driven road maintenance system is less of a database platform but still has a full operational loop. It extracts GPS information from dashcam video, interpolates coordinates for each frame, attaches detections to coordinates, and visualizes detected issues on a map with type and location. This transforms frame-level predictions into maintenance-planning artifacts rather than isolated detections (Mukherjee et al., 2021).

The production-line system is a tightly coupled perception-and-reasoning stack. Synchronized image capture ensures that all views correspond to the same vehicle pose, task-specific routing reduces unnecessary computation, view-aware fusion standardizes evidence, and the VIN-conditioned semantic rule engine compares detected evidence with the expected manifest. The final system metric is defined as

$$Acc_{\text{sys}}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}\!\left(\text{Verdict}_i=\text{GroundTruth}_i\right),$$

making the end product an interpretable PASS/FAIL decision rather than a set of disconnected detections (Kulkarni et al., 30 Sep 2025).

Other AVI systems operationalize inspection differently. Tunnel inspection converts reconstructed tunnel imagery into cylindrical projection images or planar textures, then loads them into Unity to support remote navigation, zoom, rotation, and anomaly discovery in inaccessible environments (Pahwa et al., 2019). UAV building inspection packages post-processing into a software library that estimates distances between adjacent buildings, plan shape, roof area, rooftop objects, and rooftop occupancy percentage, thereby reducing manual site access and manual measurement (Srivastava et al., 2022). In drive-by bridge inspection, the decision workflow is simulation and optimization centric: preprocess contact-point response spectra, train an adversarial autoencoder on healthy samples, compute a damage index from reconstruction error, compare healthy and damaged damage-index distributions, and optimize the vehicle parameters that maximize their separability (Hurtado et al., 3 Oct 2025).

5. Optimization, robustness, and reported performance

Quantitative evaluation in AVI is heterogeneous. RoadAtlas is notable precisely because it does not report formal quantitative evaluation results: no numerical metrics, no benchmark comparisons, no ablation studies, no loss functions, and no equations for performance are reported in the provided text (Chen et al., 2021). This absence matters because the platform is otherwise presented as deployable and economically motivated, with current road surveys cited at around $350K to $400K per survey.

Where numerical results are reported, they are task-specific. The AI-driven road maintenance pipeline reports 60.11 mAP for road damage detection, throughput of 85 FPS on an NVIDIA GTX 2080 Ti, and 0.52 mIoU for road marking segmentation on Mapillary Vistas; it also claims higher recall than traditional manual inspections, although no numerical recall value is given (Mukherjee et al., 2021). The early image-based vehicle classification system reports 98.5% for car and 98.5% for minivan in inter-class classification, plus 93% for sedan and 99.23% for taxi in the harder intra-class case (Ng et al., 2012). The production-line AVI platform reports 93% verification accuracy, 86% defect-detection recall, about 300 ms latency, and 3.3 vehicles/min throughput, while the full system reaches 98.7% feature coverage and 86.5% defect detection in the ablation table (Kulkarni et al., 30 Sep 2025).

The AVIVD idling-detection paper reports [email protected] and classwise [email protected]. On AVIVD, the earlier Real-Time IVD method using video, audio, and microphone coordinates achieves 80.97 [email protected], while AVIVD-Net removes the microphone-location input and reaches 78.89 [email protected] with MobileNetV3 audio features and 79.21 with frozen ResNet-50 audio features. On MAVD, AVIVDNet reports 55.41 [email protected], lower than several distillation-based baselines, and the paper treats that dataset primarily as a generalizability test rather than a same-setup benchmark (Li et al., 2024).

Several AVI works make optimization itself the central contribution. In drive-by bridge inspection, the vehicle mass $360^\circ$0 and tyre-suspension stiffness $360^\circ$1 are optimized by maximizing the Wasserstein distance between healthy and damaged damage-index distributions,

$360^\circ$2

The paper reports a Kriging meta-model with $360^\circ$3, an optimal vehicle with $360^\circ$4 kg, $360^\circ$5 N/m, $360^\circ$6, $360^\circ$7, and $360^\circ$8, and concludes that vehicles with frequency ratios between 0.3 and 0.7 relative to the bridge’s first natural frequency perform best, whereas near-resonant vehicles perform poorly (Hurtado et al., 3 Oct 2025).

In power transmission line inspection planning, robustness is defined as maximizing the time window during which successful re-planning after a UAV failure remains possible. The paper compares MinMax, constrained MinSum, and combined cost functions within a GRASP-based solver and reports re-planning windows such as 66% for a 500 m case and 70% for a 2000 m case under the combined cost, with all planning and re-planning times in seconds (Nekovář et al., 2023). In automated dimensional inspection of free-form auto bodies, local collision-free paths are built from approximate points, dynamic searching volumes, and spatial movement points, then globally optimized as a TSP-like problem. In the case study with 543 measuring points, simulated annealing outperformed ant colony optimization and genetic algorithms, yielding 1282.22 s optimized path measuring time, about 40% fewer dummy points, and 32% less movement time (Liu et al., 2020).

Geometric AVI also reports strong measurement accuracy. UAV-based building inspection reports average error of 0.94% for distance estimation, compared with 1.36% for Google Earth, an average roof-area difference of 4.7%, and a final rooftop occupancy percentage of 38.73%. Roof area is computed as

$360^\circ$9

Tunnel inspection reports that RepMatch yields 10,651 matches where SURF yields 31 on the same pair, that three-step pruning removed roughly 24,000 inaccurate points out of 105,000 in the trolley dataset, and that ORB-SLAM tolerated only about $36^\circ$0 rotational motion per image in a 3 m radius tunnel, while the proposed capture regime used about $36^\circ$1 per image (Srivastava et al., 2022, Pahwa et al., 2019).

6. Limitations, ambiguities, and research directions

The primary limitation across AVI is the lack of a unified problem definition. The same acronym covers road-infrastructure inspection, roadside compliance monitoring, toll-road classification, structural health monitoring, UAV infrastructure surveying, and production-line vehicle quality control. This breadth is productive, but it complicates benchmarking, dataset design, and claims of generality. A plausible implication is that AVI should be treated as a family of automated inspection architectures rather than a single benchmark task.

Many systems remain constrained by data and evaluation choices. RoadAtlas presents an operational platform but no benchmark metrics (Chen et al., 2021). The AI-driven road maintenance paper is mainly a case study and methodology paper, relies partly on proprietary datasets, and demonstrates cross-country generalization qualitatively rather than exhaustively; its barrier-safety check also depends on heuristic solidity thresholds (Mukherjee et al., 2021). The production-line AVI platform uses a mixed private/public dataset, evaluates on four models, requires a fixed 11-camera rig, and reports 93% verification accuracy rather than perfect production gating performance (Kulkarni et al., 30 Sep 2025). The early tolling-oriented classifier used only 530 images and a restricted class set (Ng et al., 2012).

Robustness to environment and domain shift remains a recurring difficulty. The road-inspection survey emphasizes lighting sensitivity, lack of explicit depth in 2-D systems, and the need for domain adaptation even in CNN-based pipelines (Fan et al., 2022). The AVIVD paper reports competitive but slightly lower performance than a prior method that used manual microphone-coordinate input, illustrating a familiar deployment trade-off between end-to-end usability and peak benchmark performance (Li et al., 2024). Tunnel inspection still depends on geometry priors and was validated on an underpass rather than a live tunnel mission (Pahwa et al., 2019). Bridge drive-by optimization currently varies only mass and stiffness while holding other vehicle properties fixed, and is demonstrated mainly on simple single-span, simply supported beams (Hurtado et al., 3 Oct 2025).

The trajectory of the field nevertheless points toward three consistent directions. First, AVI systems are becoming increasingly end-to-end, with perception coupled to databases, GIS outputs, semantic rule engines, or reconstruction environments. Second, multimodality and multi-view acquisition are displacing single-camera pipelines where operational ambiguity cannot be resolved from appearance alone. Third, planning and platform design are becoming first-class concerns: the inspection route, the sensing geometry, and even the vehicle dynamics themselves are being optimized alongside the recognition model (Chen et al., 2021, Li et al., 2024, Kulkarni et al., 30 Sep 2025, Nekovář et al., 2023, Hurtado et al., 3 Oct 2025). This suggests that, in mature AVI systems, sensing, inference, and operational decision-making are not separable subsystems but components of a single inspection stack.