Szczegóły publikacji
Opis bibliograficzny
A surface defect detection system for industrial conveyor belt inspection using apple’s true depth camera technology / Mohammad SIAMI, Przemysław Dąbek, Hamid Shiri, Tomasz BARSZCZ, Radosław Zimroz // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417 . — 2026 — vol. 16 iss. 2 art. no. 609, s. 1-23. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 20-23, Abstr. — Publikacja dostępna online od: 2026-01-07
Autorzy (5)
- AGHSiami Araghi Mohammad
- Dąbek Przemysław
- Shiri Hamid
- AGHBarszcz Tomasz
- Zimroz Radosław
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166136 |
|---|---|
| Data dodania do BaDAP | 2026-02-26 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app16020609 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh mining environments characterized by dust and variable lighting. This study introduces a smartphone-driven defect detection system for the cost-effective, geometric inspection of conveyor belt surfaces. Using Apple’s iPhone 12 Pro Max (Apple Inc., Cupertino, CA, USA), the system captures 3D point cloud data from a moving belt with induced damage via the integrated TrueDepth camera. A key innovation is a 3D-to-2D projection pipeline that converts point cloud data into structured representations compatible with standard 2D Convolutional Neural Networks (CNNs). We then propose a hybrid deep learning and machine learning model, where features extracted by pre-trained CNNs (VGG16, ResNet50, InceptionV3, Xception) are classified by ensemble methods (Random Forest, XGBoost, LightGBM). The proposed system achieves high detection accuracy exceeding 0.97 F1 score in the case of all proposed model implementations with TrueDepth F1 score over 0.05 higher than RGB approach. Applied cost-effective smartphone-based sensing platform proved to support near-real-time maintenance decisions. Laboratory results demonstrate the method’s reliability, with measurement errors for defect dimensions within 3 mm. This approach shows significant potential to improve conveyor belt management, reduce maintenance costs, and enhance operational safety.