Nowadays to detect and classify the objects from the sequence of image frames various machine learning models are used. The performance of the object recognition model is purely depending on the number of trained images. The image processing and pattern extraction focus on object recognition, localization, and classification. To classify the objects from the external events or to detect multiply objects from an image the confident level of the trained weights should be maximum. The existing object detection methods look at the specific region to detect and classify the objects in an image. The proposed object detection system takes an improved YOLOv3 model for object classification. The improved model will look at the entire image to detect and recognize the objects. The YOLOv3 model uses the neural network on the image which split the image into a region and map the confidence probability. The proposed model will detect multiple objects by exploiting the contextual information using a single CNN. The model can process 45 frames in a second and it is suitable for object detection in real-time.