The Comparison of Light Compensation Method and CycleGAN Method for Deep Learning Based Object Detection of Mobile Robot Vision Under Inconsistent Illumination Conditions in Virtual Environment
| Autor: | Jiaju Tan, Fuyong Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Brightness
Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Mobile robot 02 engineering and technology 010501 environmental sciences Object (computer science) computer.software_genre 01 natural sciences Object detection Compensation (engineering) Virtual machine 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). |
| DOI: | 10.1109/cyber46603.2019.9066709 |
| Popis: | The visual system of indoor mobile robots is mainly used for object detection, recognition, tracking, positioning and navigation. However, the effect of object detection of robot is easily affected by the outside world such as: illumination inconsistent, object occlusion, and so on. In order to explore and solve the effect of illumination inconsistent on object detection algorithm(YOLOv2) of robot vision, the virtual environment was used to simulate the real indoor environment, because the conditions are not easily controlled and susceptible to external influences for object detection in the actual indoor environment. And the low confidence of object detection of robot vision was studied and verified in the dark environment. Meanwhile, the light source was added to the robot to solve relevant problems and specific method of brightness migration based on CycleGAN is found after analyzing the principle. Finally, the method based on CycleGAN is applied to robot to improve the detection effect in dark environment. |
| Databáze: | OpenAIRE |
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