Tire Defect Detection Based on Faster R-CNN

The tire defect detection method can help the rehabilitation robot to achieve autonomous positioning function and improve the accuracy of the robot system behavior. Defects such as foreign matter sidewall, foreign matter tread, and sidewall bubbles will appear in the process of tire production, which will directly or indirectly affect the service life of the tire. Therefore, a novel and efficient tire defect detection method was proposed based on Faster R-CNN. At preprocessing stage, the Laplace operator and the homomorphic filter were used to sharpen and enhance the data set, the gray values of the image target and the background were significantly different, which improved the detection accuracy. Moreover, data expansion was used to increase the number of images and improve the robustness of the algorithm. To promote the accuracy of the position detection and identification, the proposed method combined the convolution features of the third layer and the convolution features of the fifth layer in the ZF network (a kind of convolution neural network). Then, the improved ZF network was used to extract deep characteristics as inputs for Faster R-CNN. From the experiment, the proposed faster R-CNN defect detection method can accurately classify and locate the tire X-ray image defects, and the average test recognition rate is up to 95.4%. Moreover, if there are additional types of defects that need to be detected, then a new detection model can be obtained by fine-tuning the network

Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images

Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact, with the advantages of fast speed, low cost and high precision. Firstly, typical pavement (concrete also) crack public data sets were collected, and the characteristics of sample images as well as the random variable factors, including environmental, noise and interference etc., were summarized. Subsequently, the advantages and disadvantages of three main crack identification methods (i.e., hand-crafted feature engineering, machine learning, deep learning) were compared. Finally, from the aspects of model architecture, testing performance and predicting effectiveness, the development and progress of typical deep learning models, including self-built CNN, transfer learning(TL) and encoder-decoder(ED), which can be easily deployed on embedded platform, were reviewed. The benchmark test shows that: 1) It has been able to realize real-time pixel-level crack identification on embedded platform: the entire crack detection average time cost of an image sample is less than 100ms, either using the ED method (i.e., FPCNet) or the TL method based on InceptionV3. It can be reduced to less than 10ms with TL method based on MobileNet (a lightweight backbone base network). 2) In terms of accuracy, it can reach over 99.8% on CCIC which is easily identified by human eyes. On SDNET2018, some samples of which are difficult to be identified, FPCNet can reach 97.5%, while TL method is close to 96.1%. To the best of our knowledge, this paper for the first time comprehensively summarizes the pavement crack public data sets, and the performance and effectiveness of surface crack detection and identification deep learning methods for embedded platform, are reviewed and evaluated.Comment: 15 pages, 14 figures, 11 table

Methods of assessing structural integrity for space shuttle vehicles

A detailed description and evaluation of nondestructive evaluation (NDE) methods are given which have application to space shuttle vehicles. Appropriate NDE design data is presented in twelve specifications in an appendix. Recommendations for NDE development work for the space shuttle program are presented

Effective and Efficient Non-Destructive Testing of Large and Complex Shaped Aircraft Structures

The main aim of the research described within this thesis is to develop methodologies that enhance the defect detection capabilities of nondestructive testing (NDT) for the aircraft industry. Modem aircraft non-destructive testing requires the detection of small defects in large complex shaped components. Research has therefore focused on the limitations of ultrasonic, radioscopic and shearographic methods and the complimentary aspects associated with each method. The work has identified many parameters that have significant effect on successful defect detection and has developed methods for assessing NDT systems capabilities by noise analysis, excitation performance and error contributions attributed to the positioning of sensors. The work has resulted in 1. The demonstration that positional accuracy when ultrasonic testing has a significant effect on defect detection and a method to measure positional accuracy by evaluating the compensation required in a ten axis scanning system has revealed limitsio the achievable defect detection when using complex geometry scanning systems. 2. A method to reliably detect 15 micron voids in a diffusion bonded joint at ultrasonic frequencies of 20 MHz and above by optimising transducer excitation, focussing and normalisation. 3. A method of determining the minimum detectable ultrasonic attenuation variation by plotting the measuring error when calibrating the alignment of a ten axis scanning system. 4. A new formula for the calculation of the optimum magnification for digital radiography. The formula is applicable for focal spot sizes less than 0.1 mm. 5. A practical method of measuring the detection capabilities of a digital radiographic system by calculating the modulation transfer function and the noise power spectrum from a reference image. 6. The practical application of digital radiography to the inspection of super plastically formed ditThsion bonded titanium (SPFDB) and carbon fibre composite structure has been demonstrated but has also been supported by quantitative measurement of the imaging systems capabilities. 7. A method of integrating all the modules of the shearography system that provides significant improvement in the minimum defect detection capability for which a patent has been granted. 8. The matching of the applied stress to the data capture and processing during a shearographic inspection which again contributes significantly to the defect detection capability. 9. The testing and validation of the Parker and Salter [1999] temporal unwrapping and laser illumination work has led to the realisation that producing a pressure drop that would result in a linear change in surface deformation over time is difficult to achieve. 10. The defect detection capabilities achievable by thermal stressing during a shearographic inspection have been discovered by applying the pressure drop algorithms to a thermally stressed part. 11. The minimum surface displacement measurable by a shearography system and therefore the defect detection capabilities can be determined by analysing the signal to noise ratio of a transition from a black (poor reflecting surface) to white (good reflecting surface). The quantisation range for the signal to noise ratio is then used in the Hung [1982] formula to calculate the minimum displacement. Many of the research aspects contained within this thesis are cuffently being implemented within the production inspection process at BAE Samlesbury

A real-time pothole detection based on deep learning approach

Today, the number of vehicles using the road including highways and single carriage way is increasing. road structure safety monitoring system that is safe for road users and also important to ensure long-term vehicle safety and prevent accidents due to road damage such as potholes, landslides and uneven roads. Most news reports of road accidents are also caused by potholes that are almost 10-30 cm deep, coupled with heavy rainfall that reduces visibility among drivers, significant damage to the suspension system to the vehicle or unnecessary traffic congestion. In this paper, deep learning detection with YOLOv3 algorithm is proposed apart from researches ranging from accelerometer detection, image processing or machine learning based detection as it is easier to develop and provide more accurate results. After pothole has been detected in real-time webcam, the location will be logged and displayed using Google Maps API for visualization. a total of 330 sets of data were sampled for the implementation of the pothole detection training model. As the results, the model provided 65.05 mAP and 0.9 % precision rate and 0.41 recall rate. The limitation of YOLOv3 algorithm detection can be improve further using GPU with higher specification performances and can sample 1000 to 10,000 datasets. The proposed algorithm provides acceptably high precision and efficient pothole monitoring solution under different scenarios for the users and may benefit the public and the government to monitor pothole in real-time

Effective and efficient non-destructive testing of large and complex shaped aircraft structures

The main aim of the research described within this thesis is to develop methodologies that enhance the defect detection capabilities of nondestructive testing (NDT) for the aircraft industry. Modem aircraft non-destructive testing requires the detection of small defects in large complex shaped components. Research has therefore focused on the limitations of ultrasonic, radioscopic and shearographic methods and the complimentary aspects associated with each method. The work has identified many parameters that have significant effect on successful defect detection and has developed methods for assessing NDT systems capabilities by noise analysis, excitation performance and error contributions attributed to the positioning of sensors. The work has resulted in 1. The demonstration that positional accuracy when ultrasonic testing has a significant effect on defect detection and a method to measure positional accuracy by evaluating the compensation required in a ten axis scanning system has revealed limitsio the achievable defect detection when using complex geometry scanning systems. 2. A method to reliably detect 15 micron voids in a diffusion bonded joint at ultrasonic frequencies of 20 MHz and above by optimising transducer excitation, focussing and normalisation. 3. A method of determining the minimum detectable ultrasonic attenuation variation by plotting the measuring error when calibrating the alignment of a ten axis scanning system. 4. A new formula for the calculation of the optimum magnification for digital radiography. The formula is applicable for focal spot sizes less than 0.1 mm. 5. A practical method of measuring the detection capabilities of a digital radiographic system by calculating the modulation transfer function and the noise power spectrum from a reference image. 6. The practical application of digital radiography to the inspection of super plastically formed ditThsion bonded titanium (SPFDB) and carbon fibre composite structure has been demonstrated but has also been supported by quantitative measurement of the imaging systems capabilities. 7. A method of integrating all the modules of the shearography system that provides significant improvement in the minimum defect detection capability for which a patent has been granted. 8. The matching of the applied stress to the data capture and processing during a shearographic inspection which again contributes significantly to the defect detection capability. 9. The testing and validation of the Parker and Salter [1999] temporal unwrapping and laser illumination work has led to the realisation that producing a pressure drop that would result in a linear change in surface deformation over time is difficult to achieve. 10. The defect detection capabilities achievable by thermal stressing during a shearographic inspection have been discovered by applying the pressure drop algorithms to a thermally stressed part. 11. The minimum surface displacement measurable by a shearography system and therefore the defect detection capabilities can be determined by analysing the signal to noise ratio of a transition from a black (poor reflecting surface) to white (good reflecting surface). The quantisation range for the signal to noise ratio is then used in the Hung [1982] formula to calculate the minimum displacement. Many of the research aspects contained within this thesis are cuffently being implemented within the production inspection process at BAE Samlesbury.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0