Title: A brain-inspired multibranch parallel interactive vision mechanism for advanced driver assistance systems
Authors: Wei Ou; Zihan Jin; Shiying Huang; Danlei Du; Jun Ye; Wenbao Han
Addresses: School of Cyberspace Security, Hainan University, Haikou, 570228, Hainan, China ' School of Information and Communication Engineering, Hainan University, Haikou, 570228, Hainan, China ' School of Computer Science and Technology, Hainan University, Haikou, 570228, Hainan, China ' School of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, Hainan, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, Hainan, China
Abstract: Brain-inspired research promotes the intersection and integration of brain science, computers, and other disciplines, driving a new round of scientific and technological revolution. This paper discusses the research content, features, and current status of research on brain-inspired computing and proposes a brain-inspired multibranch parallel interaction model. The feature extraction of the proposed model consists of two parallel convolutional neural networks (CNNs): the main feature extractor connected to the classifier and the auxiliary feature extractor. The latter can interact with the main feature extractor for multistage feature interaction. Additionally, four parallel branches are introduced in our CNN to learn the information on the importance of different feature map positions. Given the existing attacks on self-driving cars (in the case of shining attacks), we use the proposed model for training and detection. The experimental results show that the proposed model effectively reduces the redundant information on the feature maps, improves image classification accuracy to a certain extent, and has high learning efficiency and accuracy.
Keywords: brain-inspired; CNNs; convolutional neural networks; multibranch parallel interaction; feature extraction; shining attack.
International Journal of Sensor Networks, 2022 Vol.40 No.3, pp.203 - 216
Received: 17 Jan 2022
Accepted: 20 Jan 2022
Published online: 22 Nov 2022 *