A modified deep neural network enables identification of foliage under complex background. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Zhu, Xiaolong; Zuo, Junhao; Ren, Honge
• Is Part Of: Connection science. Volume 32:Issue 1(2020)
• Page Start: 1
• Page End: 15
• Publication Date: 2020-01-02
• Subjects: Deep neural network -- small objects -- foliage recognition -- complicated environments; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2019.1609420
• Languages: English
• ISSNs: 0954-0091

Abstract

ABSTRACT: For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. Incorporating deep residual learning module into Inception V3 can not only save the computational cost by factorising convolutions, but also mitigate the vanishing gradients causing the increasing depth of the network. Additionally, this combination can alleviate the degradation problem in the process of extracting features and providing proposals. Experimental results show that the modified approach can identify out different leaves with similar characteristics in one scene, and demonstrate the superiority of our proposed approach over some state-of-the-art deep neural networks, when it comes to recognise foliage in complicated environments.