Object Detection and Classification using Convolutional Neural Network

Khin Htay, Mie Mie Aung, Yin Cho, Moe Moe Thein

Abstract


Owing to the close relationship with the detection of objects in video learning and image recognition, many are attracted. Recent research has focused on traditional defining objects the methods are built on features of handmade trains and shallow architectures. The performance stalls easily making complex sets that combine multiple low image levels with high-level context from detectors and object views classifications with the rapid development of in-depth study, more powerful tools, we can learn semantics architecture in learning the physical things to detect. In this paper, deeper features are introduced to address the problems that exist in the area of physical object detection and classification against traditional architecture. The results show that our proposed model outperform


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