為何星瞳科技針對橋墩裂縫「單一檢測」的視覺解決方案更為可靠？

SingTown Technology基於OpenMV嘅智能相機解決方案專注於快速檢測橋墩表面裂縫。該系統利用人工智能同電腦視覺進行圖像識別，從收集到嘅橋墩圖像中提取裂縫特徵，並進行二元分類，輸出清晰嘅「檢測到裂縫/未檢測到裂縫」結果。佢適用於大規模部署中嘅檢查初步篩選、日常監測同預警場景。

In the daily inspection of bridges, one of the most fundamental and critical tasks is determining whether cracks appear on the surface of the piers. SingTown Technology's intelligent camera solution, based on the OpenMV platform, focuses on achieving the single judgment task of "crack presence." With the goal of minimising false positives and false negatives as much as possible, it provides stable and reliable visual screening capabilities.

The system captures local or overall images of the pier surface through high-resolution image acquisition, employs machine vision and image recognition algorithms for preprocessing (denoising, enhancement, contrast stretching), and extracts edge and texture features. A trained lightweight AI model determines whether linear or branching patterns matching crack characteristics exist in the images, ultimately outputting a binary classification result of "crack detected" or "no crack detected".

To enhance the robustness of detection, the algorithm incorporates artefact filtering logic, which can eliminate interference caused by shadows, stains, and light peeling, ensuring stable performance under varying lighting conditions and shooting angles. The OpenMV smart camera features low power consumption and edge deployment capabilities, allowing for long-term fixed installation on bridge piers, inspection robots, or drone-mounted platforms. It supports scheduled or triggered shooting and performs local AI-based detection on the device, reducing reliance on network connectivity while safeguarding data security.

The system's single crack detection is suitable as the first screening step in the inspection chain: once a suspected crack is identified, it can trigger manual re-inspection or a more precise crack measurement process.

SingTown applies machine vision and AI technology to this fundamental safety inspection, aiming to utilise simple and reliable embedded vision technology to reduce costs, enhance inspection efficiency, and identify potential structural risks at an early stage, thereby providing efficient visual front-end support for bridge safety management.