A Convolutional Neural Network to Perform Object Detection and Identification in Visual Large-Scale Data.

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Abstract

In recent years, big data became a hard challenge. Analyzing big data needs a lot of speed precision combination. In this article, we describe a deep learning-based method to deal with big data with a focus on precision and speed. In our case, the data are images that are the hardest type of data to manipulate because of their complex structure that needs a lot of computation power. Besides, we will solve a hard task on images, which is object detection and identification. Thus, every object in the image will be localized and classified according to the range of classes provided by the training data set. To solve this challenge, we propose an approach based on a deep convolutional neural network (CNN). Moreover, CNN is the most used deep learning model in computer vision tasks such as image classification and object recognition because of its power in self-features extraction and provides useful techniques in the prediction of decision-making. Our approach outperforms state-of-the-art models such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO (you only look once), with 77% of mean average precision on the Pascal_voc 2007 testing data set and a speed of 16.54 FPS using an Nvidia Geforce GTX 960 GPGPU.