Quantized Vision-Language Models for Damage Assessment: A Comparative Study of LLaVA-1.5-7B Quantization Levels

• URL: http://arxiv.org/abs/2603.26770v1
• Date: Tue, 24 Mar 2026 12:33:18 GMT
• Title: Quantized Vision-Language Models for Damage Assessment: A Comparative Study of LLaVA-1.5-7B Quantization Levels
• Abstract summary: This paper presents a comprehensive study of quantized Vision-Language Models (VLMs) for automated bridge damage assessment.<n>We focus on the trade-offs between description quality, inference speed, and resource requirements.<n>We develop an end-to-end pipeline combining LLaVA-1.5-7B for visual damage analysis, structured extraction, and rule-based priority scoring.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bridge infrastructure inspection is a critical but labor-intensive task requiring expert assessment of structural damage such as rebar exposure, cracking, and corrosion. This paper presents a comprehensive study of quantized Vision-Language Models (VLMs) for automated bridge damage assessment, focusing on the trade-offs between description quality, inference speed, and resource requirements. We develop an end-to-end pipeline combining LLaVA-1.5-7B for visual damage analysis, structured JSON extraction, and rule-based priority scoring. To enable deployment on consumer-grade GPUs, we conduct a systematic comparison of three quantization levels: Q4_K_M, Q5_K_M, and Q8\_0 across 254 rebar exposure images. We introduce a 5-point quality evaluation framework assessing damage type recognition, severity classification. Our results demonstrate that Q5_K_M achieves the optimal balance: quality score 3.18$\pm$1.35/5.0, inference time 5.67s/image, and 0.56 quality/sec efficiency -- 8.5% higher quality than Q4_K_M with only 4.5% speed reduction, while matching Q8_0's quality with 25% faster inference. Statistical analysis reveals Q5_K_M exhibits the weakest text-quality correlation (-0.148), indicating consistent performance regardless of description length.

Related papers

• Propagating construction-time knowledge quality into medical question answering: A framework grounded in clinical guidelines [19.37240845977001]
  We investigate whether construction-time triple quality can serve as a persistent signal for downstream evidence selection and presentation.<n>We propose a quality-aware framework that models structural (Conf) and evidential support (EvidScore) as complementary dimensions and fuses them into a per-triple quality signal, Q(t)<n> Experiments on Chinese diabetes clinical guidelines show that the utility of the quality signal is distribution dependent.
  arXiv  Detail & Related papers  (2026-08-28T14:11:20Z)
• ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling [62.983372215479925]
  Streaming and generative systems require incremental estimation from partial audio.<n>We propose ANCHOR, reformulating incremental assessment as a multi-resolution autore task.<n> Experiments show substantial robustness under partial input, including a 48% PLCMOS error reduction on 2-second prefixes.
  arXiv  Detail & Related papers  (2026-06-08T22:46:30Z)
• Fine-Tuning Vision-Language Models for Understanding Current Damage and Scoring Priority with Quality Guard Agent [0.0]
  Bridge inspection in Japan requires mandatory visual assessments every five years.<n> qualitative damage ratings (levels a-e) assigned by different engineers exhibit significant inter-rater variability.<n>This paper presents a methodology for automating bridge damage understanding and repair priority scoring.
  arXiv  Detail & Related papers  (2026-05-24T21:11:50Z)
• BiRQA: Bidirectional Robust Quality Assessment for Images [49.74447451098852]
  Full-Reference image quality assessment (FR IQA) is important for image compression, restoration and generative modeling.<n>We present BiRQA, a compact FR IQA metric model that processes four fast complementary features within a bidirectional multiscale pyramid.<n>On five public FR IQA benchmarks BiRQA outperforms or matches the previous state of the art (SOTA) while running 3x faster than previous SOTA models.
  arXiv  Detail & Related papers  (2026-02-23T20:52:56Z)
• Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images [0.1265345507370037]
  Retinal image quality assessment (RIQA) supports computer-aided diagnosis of eye diseases.<n>Most tools classify only overall image quality, without indicating acquisition defects to guide recapture.<n>This paper introduces a hybrid semi-supervised learning approach that combines manual labels for overall quality with pseudo-labels of quality details within a multi-task framework.
  arXiv  Detail & Related papers  (2025-11-17T13:17:42Z)
• PreResQ-R1: Towards Fine-Grained Rank-and-Score Reinforcement Learning for Visual Quality Assessment via Preference-Response Disentangled Policy Optimization [12.993619998545633]
  PreResQ-R1 is a Preference-Response Disentangled Reinforcement Learning framework.<n>It unifies absolute score regression and relative ranking consistency within a single reasoning-driven optimization scheme.<n>It achieves state-of-the-art results across 10 IQA and 5 VQA benchmarks under both SRCC and PLCC metrics.
  arXiv  Detail & Related papers  (2025-11-07T16:19:50Z)
• Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Models [60.356842878501254]
  Machine vision systems (MVS) are intrinsically vulnerable to performance degradation under adverse visual conditions.<n>We propose a machine-centric image quality assessment (MIQA) framework that quantifies the impact of image degradations on MVS performance.
  arXiv  Detail & Related papers  (2025-08-27T13:07:24Z)
• Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment [10.701522670464463]
  multimodal large language models (MLLMs) can proficiently evaluate visual quality through interpretable assessments.<n>We propose a unified two-stage training framework comprising a cold-start stage and a reinforcement learning-based fine-tuning stage.<n>We designate the models derived from these two stages as Q-Ponder-CI and Q-Ponder.
  arXiv  Detail & Related papers  (2025-06-03T10:11:51Z)
• ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models [48.99485386990197]
  We conduct a case study using a synthetic dataset solvable only through visual reasoning.<n>We then introduce ChartMuseum, a new Chart Question Answering (QA) benchmark containing 1,162 expert-annotated questions.<n>Although humans achieve 93% accuracy, the best-performing model Gemini-2.5-Pro attains only 63.0%, and the leading open-source LVLM Qwen2.5-VL-72B-Instruct achieves only 38.5%.
  arXiv  Detail & Related papers  (2025-05-19T17:59:27Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study [61.65123150513683]
  multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results. It is reported that these models close the robustness gap by matching the performance of supervised models trained on ImageNet. We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark.
  arXiv  Detail & Related papers  (2024-03-15T17:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.