Research Article
An Adaptive Multi-Stage Vision Framework for Defect Detection in Constrained Pipe Environments
Rahul Agnihotri*
,
Pallavi Wadhwa
Issue:
Volume 15, Issue 2, April 2026
Pages:
26-33
Received:
27 January 2026
Accepted:
26 August 2026
Published:
18 September 2026
Abstract: Visual inspection of the internal surfaces of industrial pipes is difficult to automate because the imaging conditions are hostile: illumination is non-uniform and specular, the curved surface introduces strong perspective distortion, and the working space is too confined to permit repositioning of the sensor. Purely classical image-processing pipelines respond poorly to these conditions, while single-stage deep detectors trade localization precision for speed and two-stage detectors incur a computational cost that is awkward for on-crawler deployment. This paper presents an adaptive multi-stage vision framework that combines classical image enhancement with two complementary deep detectors and resolves their outputs through an adaptive fusion stage. Raw frames are first normalized and denoised to compensate for illumination non-uniformity, after which gradient- and texture-based descriptors are extracted to expose structural discontinuities such as cracks, pitting corrosion and surface deformation. A YOLO-style detector then proposes candidate defect regions at frame rate, and a region-based convolutional network refines the surviving proposals. Rather than combining the two detectors with fixed weights, the framework computes a scalar confidence score as a convex combination of the two detector responses, with the mixing coefficients and the acceptance threshold both adapted to a per-frame estimate of image quality. This allows the system to lean on the fast detector when the frame is well illuminated and on the refinement branch when contrast collapses. The framework was evaluated on 1,200 pipe inspection images captured under varied illumination and surface conditions. It achieved 92.8% precision, 91.2% recall and 93.4% accuracy, while reducing the false positive rate to 3.1% from 9.8% for the strongest single-detector baseline. The results indicate that adaptive, quality-conditioned fusion is a more effective use of detector ensembles in constrained inspection environments than either detector alone or a fixed-weight combination.
Abstract: Visual inspection of the internal surfaces of industrial pipes is difficult to automate because the imaging conditions are hostile: illumination is non-uniform and specular, the curved surface introduces strong perspective distortion, and the working space is too confined to permit repositioning of the sensor. Purely classical image-processing pipe...
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Research Article
A GRU-based Model for Early Stroke Prediction Using Surface Electromyography Signals
Bob Chile-Agada*,
Laud Charles Ochei,
Fubara Egbono
Issue:
Volume 15, Issue 2, April 2026
Pages:
34-52
Received:
15 August 2026
Accepted:
31 August 2026
Published:
20 September 2026
DOI:
10.11648/j.ijiis.20261502.12
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Abstract: Stroke is a leading cause of mortality and long-term disability globally, necessitating early detection for timely intervention. This paper presents a comprehensive study on the development, training, and evaluation of a Gated Recurrent Unit (GRU) model for early stroke prediction using Surface Electromyography (sEMG) signals. The proposed model leverages the temporal modeling capabilities of recurrent neural networks to analyze sequential EMG data from 8 channels, intended to capture neuromuscular signal characteristics associated with stroke-induced motor impairment. The model architecture includes a GRU layer with 64 hidden units, followed by a fully connected layer with ReLU activation and a final softmax output layer for binary classification. We implement and compare two variants: a GRU-only model and a CNN-GRU hybrid. Training incorporates gradient clipping by norm and learning rate schedulers (step decay and exponential decay) to address vanishing/exploding gradient problems and optimize convergence. The model is trained and evaluated using the MUSED-I sEMG dataset, comprising 11 healthy subjects and 2 stroke patients, with a total of 11,146 windowed samples. Group K-Fold cross-validation (K=5) is used, though with only 5 resulting groups (3 healthy-file, 2 stroke-patient), some folds are single-class by construction rather than providing full subject-independent generalization evidence (see Section 3.7). The GRU-only model achieved a higher mean cross-validation accuracy (74.9%, SD 7.3%) than the CNN-GRU model (55.3%) and a Logistic Regression baseline (47.8%), though a paired comparison did not reach statistical significance (p = 0.125; see Section 5.1). An Integrated Gradients analysis on a single real test example showed differential channel importance concentrated on Channels 4 and 5; averaging across the test set is needed before generalizing this finding. The final model's Brier score (0.242) is reported for completeness, though it was computed on a single evaluation split containing no stroke-labeled test examples (see Section 4.3.2), so it reflects calibration on the negative class only; a supplementary, non-group-independent re-evaluation on a class-balanced split (Section 4.3.3) confirms the architecture can achieve genuine discrimination (ROC-AUC = 0.839, PR-AUC = 0.752) once both classes are present in the test set, at the cost of subject independence. Despite challenges related to dataset limitations and class imbalance, the results demonstrate the feasibility of GRU-based approaches for non-invasive, cost-effective stroke screening, providing a preliminary feasibility signal to guide future model refinement in a larger, prospectively validated cohort.
Abstract: Stroke is a leading cause of mortality and long-term disability globally, necessitating early detection for timely intervention. This paper presents a comprehensive study on the development, training, and evaluation of a Gated Recurrent Unit (GRU) model for early stroke prediction using Surface Electromyography (sEMG) signals. The proposed model le...
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