Computer Vision Framework for Visual Sharp Object Detection using Deep Learning Model
Nitesh Ramakrishnan1, Anandhanarayanan Kamalakannan2, Balika J Chelliah3, Govindaraj Rajamanickam4
1Nitesh Ramakrishnan, Student Trainee, Central Electronics Engineering Research Institute, Chennai Centre, CSIR Campus, Chennai (Tamil Nadu), India.
2Anandhanarayanan Kamalakannan, Central Electronics Engineering Research Institute, Chennai Centre, CSIR Campus, Chennai (Tamil Nadu), India.
3Balika J Chelliah, Department of Computer Science & Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Govindaraj Rajamanickam, Central Electronics Engineering Research Institute, Chennai Centre, CSIR Campus, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 477-481 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6415048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep learning models are widely used for visual image feature extraction and classification. Troublemakers in human society may handle sharp objects like knifes, blades to perform crimes like burglary in public places. To monitor such activities, visual sharp object detection software needs to be integrated with camera based security and surveillance systems. To implement this application, our paper discusses about computer vision framework for sharp object detection using CNN model. Initially, object detection model was built using different CNN architectures namely AlexNet, ZFNet and VGG13. In order to improve the training and testing accuracy of the above models, a new CNN model was proposed with modified VGG architecture. The proposed CNN model has limited number of convolution layers with minimum weight parameters. Thus this model improves computation efficiency when executed on Intel CPUs and delivers better accuracy in training and testing when compared with other CNN architectures. Around 98% training and 92.2% testing accuracy was obtained for this model.
Keywords: Convolutional Neural Network (CNN); Central Processing Unit (CPU); Graphical User Interface (GUI); Sharp Object Detection; Image Data Preparation
Scope of the Article: Deep Learning