[논문]Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method

Park, Min-Jun; Kim, Hyeon-June

doi:10.46670/jsst.2021.30.2.76

Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method 원문보기

Journal of sensor science and technology = 센서학회지, v.30 no.2, 2021년, pp.76 - 81

Park, Min-Jun (School of Electrical Information Communication Engineering, Kangwon National Unversity) , Kim, Hyeon-June (School of Electrical Information Communication Engineering, Kangwon National Unversity)

Abstract ▼ AI-Helper

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, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results 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.

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문제 정의

This work presents a prototype machine vision system with a high object recognition performance. By applying the multiple exposure sensing method, the prototype machine vision system can recognize the target object in a wide light intensity range without conducting the deep learning process under all light intensity conditions.

제안 방법

In this study, we propose an object recognition machine vision system based on deep learning with adopting, as a well-known technique in CISs, multiple-exposure sensing method. By effectively applying the multiple-exposure sensing technique [11- 13], the prototype machine vision system increases the object recognition rate without deep learning information in high and low illumination conditions.
2 (c), wide dynamic range (WDR) images can be obtained by synthesizing the images captured at different exposure times, which is utilized for accurate feature extraction depending on the situation. In this work, with applying MES techniques to a prototype object recognition system based on a deep learning algorithm, we attempted to verify the performance enhancement of the object recognition.
3 depicts the operational algorithm of the proposed machine vision system. The proposed system adopts three sensing modes to effectively obtain the object features: normal mode, MES mode, and WDR mode. The system operates as follows.
5 shows the prototype machine vision system model that provides information related to the target object. To verify the performance of the prototype system, deep learning was conducted using training and testing images in the ratio of 8:2. After supervised learning for image classification, the number of training repetitions was set to 200, 000.

성능/효과

of the image sensor. Nevertheless, even with the object recognition resources (based on deep learning data) with a light intensity range of only 23 dB (the light range of 70–1, 000 lux), the prototype machine vision system with multiple-exposure image sensing method was verified to improve the object recognition performance with a light intensity of up to 96 dB (the light range of 2–140, 000 lux). Moreover, by synthesizing the images captured in the MES mode, the prototype machine vision system provides a wide dynamic range performance beyond the dynamic range of the image sensor.

참고문헌 (14)

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Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method 원문보기

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Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method 원문보기

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