Research Article
AI-Driven Multi-Modal Vision Framework for Automated Detection and Quantification of Tube Blockages
Rahul Agnihotri*
,
Pallavi Wadhwa
Issue:
Volume 1, Issue 1, December 2026
Pages:
1-14
Received:
27 January 2026
Accepted:
26 August 2026
Published:
18 September 2026
Abstract: Heat exchanger tubes in power plants and refineries degrade through fouling, scaling, corrosion and wall thinning, and their internal condition governs both thermal efficiency and plant safety. Inspection of these assets is still performed largely by manual or semi-automated review of remote visual inspection footage, which is time-consuming, subjective, and prone to inconsistent defect interpretation between operators. The difficulty is compounded by the imaging environment itself: tube bores are narrow, illumination is supplied coaxially with the camera and falls off with depth, metallic surfaces produce strong specular reflections, and probe motion introduces blur. This work proposes an AI-driven multi-modal vision framework for automated detection, localization and quantification of tube blockages and surface degradation under these conditions. The framework integrates image preprocessing, deep feature learning, a hybrid convolutional neural network and vision transformer (CNN-ViT) detector, and a reinforcement learning agent that adapts probe traversal speed to the observed defect risk. A quantitative Tube Health Index (THI) is introduced to score severity from corrosion coverage, blockage ratio, defect depth and structural scaling, so that each tube is assigned an objective risk category rather than a binary defect flag. The framework was validated on an industrial dataset comprising 353 heat exchanger tubes, approximately 180 hours of footage and 12,400 labelled defect instances acquired under field conditions. The proposed hybrid model achieved 94.6% accuracy, 92.3% precision and 91.1% recall, outperforming conventional thresholding at 72.4% accuracy and a standalone CNN at 88.1% accuracy. Inference ran at 48 ms per frame on embedded hardware, and inspection time per tube fell by approximately 60% relative to manual practice. The results indicate that combining hybrid feature learning with adaptive scanning and quantitative health scoring offers a deployable pathway toward autonomous industrial non-destructive testing.
Abstract: Heat exchanger tubes in power plants and refineries degrade through fouling, scaling, corrosion and wall thinning, and their internal condition governs both thermal efficiency and plant safety. Inspection of these assets is still performed largely by manual or semi-automated review of remote visual inspection footage, which is time-consuming, subje...
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Review Article
AI-Driven Insights for Detecting Corruption Risks in Ethiopian Public Sectors: A Systematic Literature Review
Tigist Mintesnot Tewabe*
Issue:
Volume 1, Issue 1, December 2026
Pages:
15-19
Received:
26 February 2026
Accepted:
9 March 2026
Published:
22 September 2026
DOI:
10.11648/j.sdcomput.20260101.12
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Views:
Abstract: This systematic literature review examines AI-driven methods and evidence for detecting corruption risks in the public sectors of Ethiopia and neighboring East African countries. Corruption remains a significant challenge to effective public-sector governance, economic development, and the efficient delivery of public services in Ethiopia and East African countries. The increasing availability of digital government data and advances in artificial intelligence (AI) provide new opportunities to identify corruption challenges, detect anomalies, and strengthen transparency and accountability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search, screening, and selection procedures, relevant studies were identified from Scopus, Web of Science, PubMed, Google Scholar, and regional grey literature repositories. The review synthesizes evidence from 28 studies published between 2010 and 2025 that investigate the application of AI, machine learning (ML), network analysis, anomaly detection, and related computational approaches to corruption-risk identification. The review targeted on four major public-sector domains: public procurement, financial auditing, payroll management, and asset management. Key themes examined include the types and quality of data used, pre-processing and feature-engineering practices, dominant AI and ML algorithms, evaluation metrics, model interoperability, and the practical feasibility of implementation in low-resource environments. The results indicate growing interest in AI-enabled corruption detection, particularly through anomaly detection, classification, predictive modeling, and network-based approaches. However, the evidence base remains limited by fragmented datasets, insufficient data quality, limited access to government records, insufficient technical capacity, and the absence of standardized evaluation frameworks. Ethical concerns, including privacy, algorithmic bias, transparency, accountability, and the potential misuse of automated decision-support systems, also require careful consideration. In general, AI can complement, rather than replace, institutional anti-corruption mechanisms. For Ethiopia, priority should be given to strengthening public-sector data infrastructure, improving data governance and interoperability, developing interpret able and context-sensitive AI models, and establishing controlled pilot projects for procurement and financial-integrity monitoring. Sustained collaboration among policymakers, anti-corruption agencies, universities, technology experts, and international partners, together with capacity building and ethical safeguards, is essential for responsible and effective adoption of AI-driven corruption-risk detection systems.
Abstract: This systematic literature review examines AI-driven methods and evidence for detecting corruption risks in the public sectors of Ethiopia and neighboring East African countries. Corruption remains a significant challenge to effective public-sector governance, economic development, and the efficient delivery of public services in Ethiopia and East ...
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