Inspection reliability of sub-surface defects is imperative for safer functionality of critical components/materials/structures used in a wide variety of applications in various industries. The need for reliable, fast, remote, safe inspection and evaluation methods for detection of hidden defects increases in parallel with the demand for more sustainable solutions which helps in inherent modifications in design and manufacturing specifications. During in-service operation, the hidden defects are typically originated from various loading conditions leading to catastrophic failure. This perspective explores the best possible reliable, fast, remote, safe, and easy to implement for field inspection and evaluation experimental method and the associated post-processing approach using InfraRed Imaging (IRI) for Thermal Non-Destructive Testing and Evaluation (TNDT&E) of Glass Fibre Reinforced Polymer (GFRP) materials having delamination defects. This perspective further explores the state-of-the-art infrared imaging modalities used in TNDT&E by highlighting their advantages and limitations in terms of the detection sensitivity and depth resolvability to detect the subsurface defects located at interior of material. Most of the proposed experimental and post-processing techniques presented in the literature for TNDT&E, explore the observed spatial thermal contrast over the defective region of sample to provide the depth resolvability from the reconstructed thermograms even though it is obtained from the temporal data processing on the captured image sequence. This perspective provides an insight on the state-of-the-art research in the field of thermal/infrared non-destructive testing and evaluation and associated post-processing approaches to visualize the hidden subsurface defects not only resolved by spatial thermal gradients but also simultaneously provide temporal thermal gradients to locate defective regions.

This volume of Measurement Science and Technology contains a Special Feature on Industrial Vision and Automation. The special feature puts together research articles within the common denominator of Industrial Vision & Automation, especially for evaluation of various materials. This tried to demonstrate the breadth of applications for which one can use various vision systems, together with very recent research developments, some clear demonstrations of the method at work in applications and some of the necessary background theory that underpins the basic vision modalities. This special feature based on the adopted imaging methodology and the principles involved. Further articles in this Special Feature are sub-classified in to optical, thermal, X-ray and acoustical vision modalities as presented below with a brief description of the accepted manuscripts in each sub-section. Optical Vision Systems: An alternative technique is proposed to improve the visual detection rate of longitudinal conveyor belt tears by Gongxian Wang et al [1]; Automatic defect detection in multi-crystalline solar wafers using deep learning is presented by Du-Ming Tsai et al [2] Thermo-Vision Systems: An efficient denoising CAE with U-net architecture is proposed for thermal data to solve the problem of an insufficient dataset has been demonstrated in the work of Xiaoyuan Li et al [3]; Exploratory factor analysis for defect identification with active thermography is presented by Kai-Lun Huang et al [4]; X-ray Vision Systems: Coded aperture x-ray computed tomography `by sparsity-driven deterministic sampling strategy is demonstrated by Munnu Sonkar et al [5]; Acoustical Vision Systems: Phase velocity measurement of dispersive wave modes by Gaussian peak-tracing using f-k transform is presented by Bikash Ghose et al [6]; Coplanar electrical/ultrasonic dual-modality tomography for water continuous gas/oil/water three-phase distribution imaging is demonstrated by Guanghui Liang et al [7]; Defect visualization of cylindrical and conical CFRP lattice structures using rotational ultrasonic propagation imager is highlighted by J H An et al [8]; We are sure that readers will find this as a valuable special feature and a useful reference resource. Finally, we would like to thank all the authors for their contributions, the referees for their effort to thoroughly review the manuscripts by providing their valuable feedback to enhance the quality and the entire Editorial Board of Measurement Science and Technology for their constant support.