Vision based detection and classification is an emerging area of research in the field of automation. Due to the demand in automation different fields artificial intelligent architectures plays vital role to address the issues. Conventional architectures used for dealing computer vision problems are heavily under control on user features. But the new deep learning techniques have provided a substitute of automatically learning problem related features. The classification problem can be designed based on feature learned from DCNN. The performance of the DCNN algorithm vary based on the training. In this paper the performance of Deep Convolutional Neural Network (DCNN) is analyzed in classifying categories of bottle object. © 2018, Springer International Publishing AG.
cited By 0
L. Gopan and Aarthi, R., “A vision based DCNN for identify bottle object in indoor environment”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 447-456, 2018.