• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.83-90
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• 2023

Blurred image is an important factor in lowering image recognition rates in Computer vision. This mainly occurs when the camera is unstablely out of focus or the object in the scene moves quickly during the exposure time. Blurred images greatly degrade visual quality, weakening visibility, and this phenomenon occurs frequently despite the continuous development digital camera technology. In this paper, it replace the modified building module based on the Deep multi-patch neural network designed with convolution neural networks to capture details of input images and Attention techniques to focus on objects in blurred images in many ways and strengthen the image. It measures and assigns each weight at different scales to differentiate the blurring of change and restores from rough to fine levels of the image to adjust both global and local region sequentially. Through this method, it show excellent results that recover degraded image quality, extract efficient object detection and features, and complement color constancy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.1-7
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• 2012

At the industrial work site, the manufacturing process is being automated to improve work efficiency. However, it is often difficult to automate the entire manufacturing process, and there are spaces in which workers there are constantly exposed to danger. To protect such workers from the danger, this paper studied a worker safety management system for the industrial work site which uses a location recognition system and which is based on the Ubiquitous-Wireless Sensor Network (U-WSN). Using wireless signals, the distance between two devices can be measured and the location of a worker can be calculated using triangularization in 3-D. But at the industrial work sites where there are a lot of steel and structures, errors occur due to signal reflection and multi-path, etc., which makes it difficult to get the accurate location. To address this problem the following was done: first, a circular polarization patch antenna appropriate to the work site was used to reduce the degree of error that may occur from the antenna emission pattern and the particular Line of Sight (LOS); second, a 3-D localization technique and a filtering algorithm were used to improve the accuracy of location determination. The developed system was tested by using it on a wharf crane to validate its accuracy and effectiveness. The proposed location recognition system is expected to contribute greatly in ensuring the safety of workers at industrial work sites.