# Survey of Quantization Techniques for On-Device Vision-based Crack Detection

arXiv: https://arxiv.org/abs/2502.02269 · HTML: https://mlsanity.com/paper/2502.02269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications](https://mlsanity.com/paper/2203.15069/) (2022)

## Built on by (most cited first, 2 of 2)

- [On-Device Crack Segmentation for Edge Structural Health Monitoring](https://mlsanity.com/paper/2505.07915/) (2025)
- [LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines](https://mlsanity.com/paper/2506.06346/) (2025)

## Abstract (arXiv)

Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection. Vision-based crack detection, combined with UAVs, addresses the limitations of traditional sensor-based SHM methods but requires the deployment of efficient deep learning models on resource-constrained devices. This study evaluates two lightweight convolutional neural network models, MobileNetV1x0.25 and MobileNetV2x0.5, across TensorFlow, PyTorch, and Open Neural Network Exchange platforms using three quantization techniques: dynamic quantization, post-training quantization (PTQ), and quantization-aware training (QAT). Results show that QAT consistently achieves near-floating-point accuracy, such as an F1-score of 0.8376 for MBNV2x0.5 with Torch-QAT, while maintaining efficient resource usage. PTQ significantly reduces memory and energy consumption but suffers from accuracy loss, particularly in TensorFlow. Dynamic quantization preserves accuracy but faces deployment challenges on PyTorch. By leveraging QAT, this work enables real-time, low-power crack detection on UAVs, enhancing safety, scalability, and cost-efficiency in SHM applications, while providing insights into balancing accuracy and efficiency across different platforms for autonomous inspections.