Research on the identification of obstacle image based on convolutional neural network

Zhongyu Li; Huajun Wang; Yuhang Cao; Yuting Dai

doi:10.1117/12.2600363

1 June 2021 Research on the identification of obstacle image based on convolutional neural network

Zhongyu Li, Huajun Wang, Yuhang Cao, Yuting Dai

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184807 (2021) https://doi.org/10.1117/12.2600363
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convolutional neural network to obtain the high-level image features, which are used to construct the more accurate neural network model parameters and to train the softmax classifier with these parameters. Then the distance between the imported images and the data images is calculated by using the softmax classifier, and the similarity between the obstacles and non-obstacles is estimated. The experimental results show that the discrimination accuracy increase to above 94%. And the proposed method is more effectively compared with traditional ultrasonic and radar methods.

Citation Download Citation

Zhongyu Li, Huajun Wang, Yuhang Cao, and Yuting Dai "Research on the identification of obstacle image based on convolutional neural network", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184807 (1 June 2021); https://doi.org/10.1117/12.2600363

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