Simultaneous tailoring of pulsed thermography experimental and processing parameters for enhanced defect detection and sizing in adhesive bonds in carbon fibre composites

Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8 % on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1- 2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.

Evidential regression-based blood glucose detection using waveform features

<div>As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We analyzed the characteristics of the signal amplitude decay in the photoacoustic signal and employed the forced damping vibration equation to model the signal waveform. A new waveform feature was constructed to describe the amplitude attenuation rate. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, in human tissue and glucose solution experiments, the minimum error is achieved 1.02±0.71 mg/dL and 13.28±10.33 mg/dL, respectively.</div><div><br></div>

Evidential regression-based blood glucose detection using waveform features

<div>As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We analyzed the characteristics of the signal amplitude decay in the photoacoustic signal and employed the forced damping vibration equation to model the signal waveform. A new waveform feature was constructed to describe the amplitude attenuation rate. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, in human tissue and glucose solution experiments, the minimum error is achieved 1.02±0.71 mg/dL and 13.28±10.33 mg/dL, respectively.</div><div><br></div>

Material characterisation by enhanced resolution in non-stationary thermal wave imaging

In the recent past, quadratic frequency-modulated thermal wave imaging (QFMTWI) has been advanced with a chirp z-transform (CZT)-based processing approach to facilitate enhanced subsurface anomaly detection, depth quantification and material property estimation with enhanced depth resolution. In the present study, the applicability of CZT-based phase analysis for foreign object defect detection in a structural steel sample using QFMTWI is validated through finite element-based numerical modelling rather than experimental verification due to limited available resources. Furthermore, the enhanced defect detection capability of the CZT phase approach is qualitatively compared with the frequency- and time-domain phase approaches using the defect signal-to-noise ratio (SNR) as a quality metric. Also, an empirical relationship between the observed phases and the thermal reflection coefficient is obtained, which recommends the CZT phase as a prominent approach for foreign material defect detection.

A new borehole electromagnetic receiver developed for controlled-source electromagnetic methods

Conventional surface electromagnetic methods have limitations of a shallow detection depth and low resolution. To increase the detection depth and resolution, borehole–surface electromagnetic methods for electromagnetic three-dimensional observations of the ground, tunnels, and boreholes have been developed. Current borehole receivers only measure a single parameter of the magnetic field component, which does not meet the special requirements of controlled-source electromagnetic (CSEM) methods. This study proposes a borehole electromagnetic receiver that realizes synchronous acquisition of the vertical electric field component in the borehole and the three-axis orthogonal magnetic field components. This receiver uses Ti electrodes and fluxgate magnetometers (fluxgates) as sensors to acquire electric and magnetic field components. Multi-component comprehensive observation methods that add the electric field component can effectively support the CSEM method, improve detection accuracy, and exhibit a strong potential for detecting deep ore bodies. We conducted laboratory and field experiments to verify the performance of our new borehole electromagnetic receiver. The receiver achieved a magnetic field noise of less than 6 pTHz-1/2 at 1 kHz, and the electric field noise floor was approximately 20 nVm-1Hz-1/2 at 1 kHz. The −3 dB electric field bandwidth can reach DC −10 kHz. The results of our experiments prove that high-quality CSEM signals can be obtained using this new borehole electromagnetic receiver and that the electric field component exhibits sufficient advantages for measuring the vertical component of the electric field.

Research on the Infrared Thermographic Detection of Concrete under Solar Heating

Infrared thermography for detecting defects in concrete structures is closely related to the heat source and the optimized method of the thermal image. Due to the limitation of the irradiation area of the heat source, it is inefficient to detect the defects in large concrete structures. In this paper, sunlight was employed as a heat source to detect the defects with different sizes and depths in concrete, and the measured infrared images were processed and optimized by an enhancement algorithm. The experimental results showed that the defects in concrete could be rapidly identified under sunlight. The effect of environment, view angle, and boundary can be eliminated by image preprocessing, and the histogram equalization algorithm can increase the detection depth of the defects. The research results can also provide a reference for the infrared detection technology of concrete under the weak heat source.

Research on Golay-coded excitation in real-time imaging of high frequency ultrasound biomicroscopy

High frequency ultrasonic imaging provides clinicians with high-resolution diagnostic images and more accurate measurement results. The technique is now widely used in ophthalmology, dermatology, and small animal imaging. However, since ultrasonic attenuation in tissue increases rapidly with increasing frequency, the depth of detection of high frequency ultrasound in tissue is limited to a few millimeters. In this paper, a novel method of using Golay-coded excitation as a replacement for conventional single-pulse excitation in high frequency ultrasound biomicroscopy was proposed, and real-time imaging was realized. While maintaining the transmission voltage and image resolution unchanged, the detection depth can be effectively improved. The ultrasonic transmission frequency is 30 MHz and the transmission voltage is ± 60 V p-p. In this study, 4-bit, 8-bit, and 16-bit coding sequences and decoding compression were used. To verify the effectiveness of the coding sequence in real-time imaging of ultrasound biomicroscopy, we designed a 10-μm diameter line target echo experiment, an ultrasound phantom experiment, and an in vitro porcine eye experiment. The experimental results show that the code/decode method of signal processing can not only maintain a resolution consistent with that of single-pulse transmission, but can also improve the detection depth and signal-to-noise ratio.

WGI-Net: A weighted group integration network for RGB-D salient object detection

Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.