An Efficient Methodology for Object Classification Using Light Weight Deep Convolutional Neural Networks
Anjanadevi B1, S Naga Kishore Bhavanam2, E Srinivasa Reddy3 

1Anjanadevi B, Research Scholar, Acharya Nagarjuna University, Guntur, India.
2Dr. S Nagakishore Bhavanam, Assistant Professor, Dept. of ECE, Acharya Nagarjuna University, Guntur, India.
3Dr. E Srinivasa Reddy, Professor, Deparment of CSE, Acharya Nagarjuna University, Guntur, INDIA.

Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 5965-5968 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3608078219/19©BEIESP | DOI: 10.35940/ijrte.B3608.078219
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Abstract: In current era, deep convolution neural networks (DCNNs) have good break-through in processing images while reducing computational cost and increasing accuracy. Proposed approach focuses on object detection using classification with DCNN model. This model uses feature map for pre-processing the images and convolution layers helps to minimize the processing using deep learning perceptron’s. After that the proposed approach uses Light – Weight Deep Convolution Neural Network(LW_DCNN) Model which includes less number of convolution layers, Max Pooling layers with relevant parameters and Dense, flatten layers to train the data using Leaky ReLU function for improving accuracy. The proposed methodology LW_DCNN is highly efficient compared to traditional classification techniques and presenting simple and powerful model for object detection in Video Surveillance Systems. This model also tested on GPU systems and proved efficiency in less computational time. Obtained Results are clearly shows that model is more efficient in classifying the objects intern classifying the working condition of the overhead power polls insulators in real time video frame sequences.
Index Terms: Deep Convolution Neural Networks (DCNN), Light – Weight DCNN Model (LW_DCNN), Leaky ReLU, AMPWC (Auto Monitoring of Product Working Condition).

Scope of the Article: Classification