  Existing computer vision and object detection methods strongly rely on neural
networks and deep learning. This active research area is used for applications
such as autonomous driving, aerial photography, protection, and monitoring.
Futuristic object detection methods rely on rectangular, boundary boxes drawn
over an object to accurately locate its location. The modern object recognition
algorithms, however, are vulnerable to multiple factors, such as illumination,
occlusion, viewing angle, or camera rotation as well as cost. Therefore, deep
learning-based object recognition will significantly increase the recognition
speed and compatible external interference. In this study, we use convolutional
neural networks (CNN) to recognize items, the neural networks have the
advantages of end-to-end, sparse relation, and sharing weights. This article
aims to classify the name of the various object based on the position of an
object's detected box. Instead, under different distances, we can get
recognition results with different confidence. Through this study, we find that
this model's accuracy through recognition is mainly influenced by the
proportion of objects and the number of samples. When we have a small
proportion of an object on camera, then we get higher recognition accuracy; if
we have a much small number of samples, we can get greater accuracy in
recognition. The epidemic has a great impact on the world economy where
designing a cheaper object recognition system is the need of time.
