An efficient and hybrid pulse coupled neural network - based object detection framework based on machine learning. (December 2021)

• Record Type: Journal Article
• Authors: S, Dharini; Jain, Sanjay
• Is Part Of: Computers & electrical engineering. Volume 96:Part B(2021)
• Publication Date: 2021-12
• Subjects: Image fusion -- Pulse coupled neural network (PCNN) -- Discrete cosine transform (DCT) -- Convolutional Neural Network (CNN) -- Object detection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107615
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The objective of fusing Infrared (IR) and Visible Image (VI) is to obtain essential information and reproduce an image with high reliability for human vision. Existing fusion methods are characterized by loss of information in fusion process thereby leading to lack of precision. To preserve the information, a novel fusion method is proposed in this research work by utilizing a pulse coupled neural network-based image fusion methodology. Proposed work integrates the visible image and IR image and generates a fused image with enhanced information. Further, the fused image and non-image data are used to detect the query objects like human and other objects using a convolutional neural network model. The proposed work is apt and suitable for surveillance applications, to analyze the scene in an effective manner. Experimental results reflect a superior performance as compared against benchmark methods in extracting the target information whilst preserving the visible background image.