Defect detection technologies and tools have become increasingly automated over the years, anchored in the striking strides in deep learning techniques. Advances in automated visual-based defect detection approaches have expanded the avenues for various end-use industries in the defect detection market. Businesses in industrial production lines notably in automotive and manufacturing sectors have harnessed deep learning-based algorithms for improving the overall process of defect detection in terms of speed, accuracy, and the complexity of targets.
The advent of deep-learning models for defect detection for range of material surfaces, such as metals, stone, ceramics, and textiles has opened up new growth trajectories in the defect detection market. State-of-the art deep learning techniques empowered by computing systems-inspired artificial neural networks have helped end users to ensure quality control and assurance.
Growing adoption of ICV and machine vision in the non-destructive defect detection in manufacturing and construction industries respectively are enriching the revenue prospects for players in the defect detection market. Some of the major non-destructive defect detection technologies used in industrial production line for quality assurance sit under visual-based approaches, dye penetrant inspection, radiography, eddy current approach, and ultrasonic testing.
Automated defect detection techniques have grown in significance in smart supply chain, since their application enables early detection of defective products and save production costs. The strides in the smart manufacturing and adoption of new operating models for quality assurance are constantly enriching the growth of the defect detection market.
A growing body of research in current detection algorithms have explored deep learning models for targets that are small and complex. Newer visual-based approaches for defect detection are gathering traction in industrial applications, unlocking new prospects of the defect detection market. Players in the market have been garnering revenue gains from the rapid penetration of defect detection technologies in aeronautics. Intensive studies on the taxonomy of defects in metals have opened up new opportunities for players in the defect detection market.
In steel manufacturing, there is massive market prospects for improved detection algorithms. Traditional methods have been fraught with low confidence, high labor intensity, and low production needs. Thus, global producers in the steel industry are keen on leveraging the recent advancements in deep learning methods in defect detection. This is a key driver for the innovations in the defect detection market.
A number of studies son nondestructive defect detection in the Industry 4.0 are enriching the prospects of the defect detection market. A team of researchers in China have demonstrated a novel steel surface defect detection, the results of which are published in March 2021. The investigators harnessed deformable convolution network and applied Fast R-CNN algorithm for object detection. They demonstrated that the new model was effective in identifying small target defects on the steel surfaces. The new method is aimed at improving detection algorithms over NEU-DET dataset detection. Researchers in various parts of the world are researching on data-driven artificial intelligence techniques for developing state-of-the art automated defect detection, thus paving the way to new avenues in the defect detection market.
New platforms have come to the fore to cater to the need for faster and accurate defect detection methods in mass production models are key to unlocking new prospects of the defect detection market. Some of the key players in the defect detection market are IBM, Omron, Microsoft, Datalogic, Cognex Corp, Teledyne Technologies, and Amazon Web Services.
Asia Pacific is one of the lucrative regions, where emerging economies have emerged as the hotbed of opportunities. The growth is propelled by the strides in the Industry 4.0 and enormous studies being undertaken on artificial visual processing techniques, notably deep detection methods. North America has witnessed a vast appetite for state-of-the art techniques in manufacturing and aeronautics sector.
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