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Microwave and millimetre-wave non-destructive testing and evaluation (NDT&E) has a long history dating back to the late 1950s (Bahr 1982 Microwave non-destructive testing methods ; Zoughi 2000 Microwave Non-destructive testing and evaluation principles ; Feinstein 1967 Surface crack detection by microwave methods ; Ash 1973 In 3rd European Microwave Conference ; Auld 1981 Phys. Technol. 12 , 149–154; Case 2017 Mater. Eval. 75 ). However, sustained activities in this field date back to the early 1980s (Zoughi 1995 Res. Nondestr. Eval. 7 , 71–74; Zoughi 2018 Mater. Eval. 76 , 1051–1057; Kharkovsky 2007 IEEE Instrumentation & Measurement Magazine 10 , 26–38). Owing to various limitations associated with using microwaves and millimetre waves for NDT&E, these techniques did not see much utility in the early days. However, with the advent and prevalence of composite materials and structures, in a wide range of applications, and technological advances in high-frequency component design and availability, these techniques are no longer considered as ‘emerging techniques’ (Zoughi 2018 Mater. Eval. 76 , 1051–1057; Schull 2002 Nondestructive evaluation: theory, techniques, and applications ). Currently, microwave and millimetre-wave NDT&E is a rapidly growing field and has been more widely acknowledged and accepted by practitioners over the last 25+ years (Case 2017 Mater. Eval. 75 ; Bakhtiari 1994 IEEE Trans. Microwave Theory Tech . 42 , 389–395; Bakhtiari 1993 Mater. Eval. 51 , 740–743; Bakhtiari 1993 IEEE Trans. Instrum. Meas. 42 , 19–24; Ganchev 1995 IEEE Trans. Instrum. Meas. 44 , 326–328; Bois 1999 IEEE Trans. Instrum. Meas. 48 , 1131–1140; Ghasr 2009 IEEE Trans. Instrum. Meas. 58 , 1505–1513). Microwave non-destructive testing was recently recognized and designated by the American Society for Nondestructive Testing (ASNT) as a ‘Method’ on its own (Case 2017 Mater. Eval. 75 ). These techniques are well suited for materials characterization; layered composite inspection for thickness, disbond, delamination and corrosion under coatings; surface-breaking crack detection and evaluation; and cure-state monitoring in concrete and resin-rich composites, to name a few. This work reviews recent advances in four major areas of microwave and millimetre-wave NDT&E, namely materials characterization, surface crack detection, imaging and sensors. The techniques, principles and some of the applications in each of these areas are discussed. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.

In the process of large-scale industrial production, in order to control the surface accuracy and quality of components, it is necessary to carry out efficient detection of surface cracks. However, traditional manual visual inspection and other means are not conducive to large-scale industrial utilization due to the subjective human factors such as personnel experience and level. Based on this, this paper first analyses the basic principle of computer image recognition, and then studies the utilization of computer image recognition in surface crack detection, and gives the specific utilization steps, utilization methods and detection results.

Saw wood cracks are defects that affect the appearance and mechanical strength of sawn wood. Crack defects in the surface of sawn wood can be readily detected. Decisions regarding the presence and severity of such defects can affect the utilization rate of sawn timber. Due to the heavy workload, low efficiency, and low accuracy of manual inspection, traditional machine learning methods have strong specialization, complex methods, and high costs. By studying the semantic segmentation model of surface crack defects in sawn timber based on deep learning, the optimal model for segmentation and detection of surface cracks in sawn timber was established. The improved Attention U-Net model encoding stage was introduced into CBAM, and AdamW optimization was used instead of SGD and Adam to achieve better crack semantic segmentation results. The ECA module was introduced in the skip connection part, and the weighted fusion multi loss function was used instead of the original cross entropy loss function. The positions of the two modules were replaced to improve the accuracy of semantic segmentation of surface cracks in sawn timber. Through comparative experiments, the improved model also achieved higher scores in semantic segmentation indicators for surface cracks in sawn timber compared to other models.

Cracks in building facades are inevitable due to the age of the building. Cracks found in the building facade may be further exacerbated if not corrected immediately. Considering the extensive size of some buildings, there is definitely a need to automate the inspection routine to facilitate the inspection process. The incorporation of deep learning technology for the classification of images has proven to be an effective method in many past civil infrastructures like pavements and bridges. There is, however, limited research in the built environment sector. In order to align with the Smart Nation goals of the country, the use of Smart technologies is necessary in the building and construction industry. The focus of the study is to identify the effectiveness of deep learning technology for image classification. Deep learning technology, such as Convolutional Neural Networks (CNN), requires a large amount of data in order to obtain good performance. It is, however, difficult to collect the images manually. This study will cover the transfer learning approach, where image classification can be carried out even with limited data. Using the CNN method achieved an accuracy level of about 89%, while using the transfer learning model achieved an accuracy of 94%. Based on this, it can be concluded that the transfer learning method achieves better performance as compared to the CNN method with the same amount of data input.

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry.

Concrete surface crack detection plays a crucial role in infrastructure maintenance and safety. Deep learning-based methods have shown great potential in this task. However, under real-world conditions such as poor image quality, environmental interference, and complex crack patterns, existing models still face challenges in detecting fine cracks and often rely on large training parameters, limiting their practicality in complex environments. To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. Specifically, the maximum soft pooling module improves the continuity and integrity of detected cracks. The adaptive crack feature quantization module enhances the contrast between cracks and background features and strengthens the model’s focus on critical regions through spatial feature fusion. The trainable crack post-processing module incorporates edge-guided post-processing algorithms to correct false predictions and refine segmentation results. Experiments conducted on the Crack500 Road Crack Dataset show that, the proposed model achieves notable improvements in detection accuracy and efficiency, with an average F1-score improvement of 2.81% and a precision gain of 2.20% over the baseline methods. In addition, the model significantly reduces computational cost, achieving a 78.5–88.7% reduction in parameter size and up to 96.8% improvement in inference speed, making it more efficient and deployable for real-world crack detection applications.

Surface cracks are typical defects in high-speed rail (HSR) slab tracks, which can cause structural deterioration and reduce the service reliability of the track system. However, the question of how to effectively detect and quantify the surface cracks remains unsolved at present. In this paper, a novel crack-detection method based on infrared thermography is adopted to quantify surface cracks on rail-track slabs. In this method, the thermogram of a track slab acquired by an infrared camera is first processed with the non-subsampled contourlet transform (NSCT)-based image-enhancement algorithm, and the crack is located via an edge-detection algorithm. Next, to quantitatively detect the surface crack, a pixel-locating method is proposed, whereby the crack width, length, and area can be obtained. Lastly, the detection accuracy of the proposed method at different temperatures is verified against a laboratory test, in which a scale model of the slab is poured and a temperature-controlled cabinet is used to control the temperature-change process. The results show that the proposed method can effectively enhance the edge details of the surface cracks in the image and that the crack area can be effectively extracted; the accuracy of the quantification of the crack width can reach 99%, whilst the accuracy of the quantification of the crack length and area is 85%, which essentially meets the requirements of HSR-slab-track inspection. This research could open the possibility of the application of IRT-based track slab inspection in HSR operations to enhance the efficiency of defect detection.

Surface crack detection and sizing is essential for the manufacturing and maintenance of engines, run parts, and other metal elements of aircrafts. Among various non-destructive detection methods, the fully non-contact and non-intrusive technique based on laser-stimulated lock-in thermography (LLT) has recently attracted a lot of attention from the aerospace industry. We propose and demonstrate a system of reconfigurable LLT for three-dimensional surface crack detection in metal alloys. For large area inspection, the multi-spot LLT can speed up the inspection time by a factor of the number of spots. The minimum resolved size of micro-holes is ~50 µm in diameter limited by the magnification of the camera lens. We also study the crack length ranging from 0.8 to 3.4 mm by varying the modulation frequency of LLT. An empirical parameter related to the thermal diffusion length is found to show the linear dependence with the crack length. With the proper calibration, this parameter can be used to predict the sizing of the surface fatigue cracks. Reconfigurable LLT allows us to quickly locate the crack position and accurately measure its dimensions. This method is also applicable to the non-destructive detection of surface or sub-surface defect in other materials used in various industries.

Polycrystalline diamond compact (PDC) bits are commonly used drill bits in the petroleum drilling industry. Cracks often occur on the surface of a bit, which may result in the unexpected suspension of the drilling operation, or even accidents. Therefore, the detection of surface cracks on PDC bits is of great importance to ensure continuous drilling operation and to prevent accidents. However, it is extremely difficult to detect such cracks by visual inspection or other traditional nondestructive testing (NDT) techniques due to the small size of cracks and the irregular geometry of bits. As one emerging NDT technique, eddy current pulsed thermography (ECPT) can instantly detect surface cracks on metal parts with irregular geometry. In this study, the feasibility of ECPT of detecting surface cracks on the tungsten carbide matrix of PDC bits was investigated. A successive scanning detection mode is proposed to detect surface cracks by using ECPT with a low power heating excitation unit and small-size coils. The influence of excitation duration on the detection result was also investigated. In addition, principal component analysis (PCA) was employed to process the acquired IR image sequences to improve detection sensitivity. Finally, the whole shape of a crack was restored with processed images containing varied cracks segments. Based on the experimental results, we conclude that the surface cracks on the tungsten carbide matrix of PDC bit can be detected effectively and conveniently by ECPT in scanning mode with the aid of PCA.