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
DOI:
10.11648/j.ijiis.20261502.11
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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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