Native Monkey Detection Using Deep Convolution Neural Network
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we present development and analysis of deep learning architectures for real life and real-time native indian monkey (langur) detection. our monkey detection work is motivated with the idea of developing an application which will assist farmers in protecting their crops from attack of native wild animals. protecting destruction of crops from wild animals is very important for agrarian societies. we engineered pre-trained vgg19 convolution neural network (cnn) to detect live monkeys in images streamed in an image sequence generated from a usb webcam. we used transfer learning approach to custom train, yolo, a much faster cnn architecture to detect monkeys. for training and analysis, we used the google open images v4 dataset. we give details of how two significantly different architectures were customized for native monkey detection. the performance of the detectors developed is analysed in terms of time taken for detection and accuracy of detection. custom trained architecture achieved high true positive rate of 97.19% and true negative rate of 95.32%, which are significantly higher and better than the pre-trained architecture. the developed system was successfully tried out on a 4gb ram and intel quadcore i3 laptop at indroda national park.
KeywordsObject detector Convolution neural network Deep learning Real time
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