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Recent advances in surface defect inspection of industrial products using deep learning techniques
Manual surface inspection methods performed by quality inspectors do not satisfy the continuously increasing quality standards of industrial manufacturing processes. Machine vision provides a solution by using an automated visual inspection (AVI) system to perform quality inspection and remove defective products. Numerous studies and works have been conducted on surface inspection algorithms. With the advent of deep learning, a number of new algorithms have been developed for better inspection. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. In particular, we focus on the inspection of industrial products in semiconductor, steel, and fabric manufacturing processes. This work makes three contributions. First, we present the prior literature reviews on vision-based surface defect inspection and analyze the recent AVI-related hardware and software. Second, we review traditional surface defect inspection algorithms including statistical methods, spectral methods, model-based methods, and learning-based methods. Third, we investigate recent advances in deep learning-based inspection algorithms and present their applications in the steel, fabric, and semiconductor industries. Furthermore, we provide information on publicly available datasets containing surface image samples to facilitate the research on deep learning-based surface inspection.
Journal Article
Utilization and Verification of Imaging Technology in Smart Bridge Inspection System: An Application Study
Image-based inspection technologies involving various sensors and unmanned aerial vehicles are widely used for facility inspections. The level of data analysis technology required to process the acquired data algorithmically (e.g., image processing and machine learning) is also increasing. However, compared with their development rate, the applicability of new inspection technologies to actual bridges is low. In addition, only individual technologies (for inspecting specific deteriorations) are being developed; integrated inspection systems have been neglected. In this study, the bottom-up method (which systematizes the applications of a specific technology) is avoided; instead, several technologies are summarized and a system of preliminary frameworks is established using a top-down method, and the applicability of each technology is verified in a testbed. To this end, the utility of the initially constructed technical system was assessed for two bridges; then, a strong utility technology was selected and applied to an offshore bridge under extreme conditions. The data obtained from the inspection were accumulated in a database, and a 3D-type external inspection map was produced and applied in the subsequent inspection via virtual and augmented reality equipment. Through the system, it was possible to obtain cost-effective and objective bridge inspection images in extreme environments, and the applicability of various technologies was verified.
Journal Article
UK: Mitie launches new specialised drone service
In June, Mitie announced the launch of a new drone service. The technology enables improved property surveying, efficient thermal mapping and the inspection of high-rise buildings -- previously unreachable. The drone inspection service offers unrivalled benefits in terms of quality of the inspection, cost reduction and instant reporting. The drone's hi-resolution 4K camera boosts the accuracy of each survey giving facilities and property managers a more detailed inspection service. With the ability to reach 400ft, previously inaccessible places are reachable from the ground and without the need for specialist equipment. Footage and imagery of any areas of concern are immediately transmitted to the operative's smartphone or tablet for inspection.
Journal Article