Chen Qi
4 Papers
20 Citations
Chen Qi is an academic researcher. The author has contributed to research in topics: Pixel & Obstacle. The author has an hindex of 3, co-authored 4 publications.
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Papers
Patent
Traffic sign detection method in natural scene
Li Wenju,Chen Qi,Lu Yunfan,Hu Wenkang,Zhang Meng +4 more
- 27 Oct 2017
TL;DR: In this paper, a traffic sign detection method in a natural scene is presented, which comprises the steps that a detection image shot in the natural scene was acquired; luminance information of the detection image is subjected to statistical analysis, different luminance areas are divided according to grade luminance threshold values, pixel ratios of different luminances areas are calculated respectively, and the image is divided into a dark scene, a bright scene, backlighting scene and a normal scene according to all the pixel ratios and scene classification threshold values; gamma parameter values are selected according to scene classification results.
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Patent
Safe travel device suitable for phubbing on roads
Sun Jingyi,Li Wenju,Chen Qi,Lu Yunfan,Li Jianmo +4 more
- 15 Feb 2017
TL;DR: In this article, a safe travel device suitable for phubbing on roads is presented, which consists of a depth image collection module, a sound collection module and a microprocessor.
7
Patent
Safe travel device suitable for smartphone addicts on road and obstacle avoidance method of safe travel device
Sun Jingyi,Li Wenju,Chen Qi,Lu Yunfan,Li Jianmo +4 more
- 31 May 2017
TL;DR: In this paper, the authors proposed a safe travel device for smartphone addicts on a road and an obstacle avoidance method of the safe travel devices, which includes a sensor acquisition module, a microprocessor module, power module and an output module.
3
Patent
Traffic sign recognition method and device based on improved Hu invariant moment and ELM
Li Wenju,Chen Qi,Lu Yunfan,Hu Wenkang,Zhang Meng +4 more
- 22 Sep 2017
TL;DR: Hu et al. as discussed by the authors proposed a traffic sign recognition method based on an improved Hu invariant moment and an extremity learning machine (ELM) neural network, and the traffic sign was recognized via the trained ELM neural network.
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