Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method
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초록
In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, shortand long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. Thatresults in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.
Abstract
In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, shortand long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. Thatresults in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.
참고문헌 (14)
- M. Minsky, "Steps toward Artificial Intelligence", Proc. of IEEE, 49(1), pp.8~30 (1961)
- K. Robert, "Artificial Intelligence and Human Thinking", International Joint Conference on Artificial Intelligence, 1(11), pp.11~16 (2011)
- S. Russell, "Artificial Intelligence: a Modern Approach", pp.1~932 (2009), Prentice Hall Press
- Y. LeCun, "Deep learning", Nature, 521, pp.436~444 (2015)
- I. Goodfellow, "Deep Learning" (2016), Massachusetts Institute of Technology Press
- P. Y. Simard, "Best practices for convolutional neural networks applied to visual document analysis", Proc. of IEEE Conf. on August. 7th International Conference on Document Analysis and Recognition, pp.958~963 (2003)
- H. Kim, "CMOS image sensor for wide dynamic range feature extraction in machine vision", The Institution of Engineering and Technology. IET, 57(5), pp.206~208 (2020)
- H. Kim, "A DualImaging Speed-Enhanced CMOS Image Sensor for RealTime Edge Image Extraction", Journal of Solid-State Circuits, 52(9), pp.2488~2497 (2017)
- H. Kim, "A Delta-Readout Scheme for Low-Power CMOS Image Sensors With Multi-ColumnParallel SAR ADCs", Journal of Solid-State Circuits, 51(10), pp.2262~2273 (2016)
- H. Kim, "A Sun-Tracking CMOS Image Sensor with Black-Sun Readout Scheme", Trans. Electron Devices, 68(3), pp.1115~1120 (2021)
- M. Mase, "A wide dynamic range CMOS image sensor with multiple exposure-time signal outputs and 12-bit columnparallel cyclic A", D converters, 2005, pp.40(12), 2787~2795
- J. Chun, "Dynamic-Range Widening in a CMOS Image Sensor Through Exposure Control Over a Dual-Photodiode Pixel", Trans. Electron Devices, 56(12), pp.3000~3008 (2009)
- D. Stoppa, "A 120-dB Dynamic Range CMOS Image Sensor with Programmable Power Responsivity", Proc. of IEEE Conf. the 32nd European Solid-State Circuits, pp.420~423 (2006)
- J. Park, "A 0.1e- vertical FPN 4.7e- read noise 71dB DR CMOS image sensor with 13b column-parallel single-ended cyclic ADCs", IEEE Conf. International Solid-State Circuits, pp.268~269 (2009)
