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In this technological era, computer vision accomplishes superior performance and it introduced to the Convolutional neural network (CNN) which is acquainted for recognize an object. In addition to object detection and classification are considered difficult tasks in computer vision. From the ancient times vehicle is the only communication media to move from one place to another place. In this automation period local vehicles are decreasing day by day. Because these type of vehicles motion are slow. We want to preserve our culture with the help of vehicle-NN model. We presented our own CNN model name Vehicle-NN which will be a convenient effect on the vehicle sector of Bangladesh. We compared our CNN model with other pre-trained models like MobileNet, VGG16, InceptionV3. This proposed model plays an effective role in the future automation vehicle identification sector. Besides we want to hold on to our traditional roots and preserving our local culture. Our work approach is novel for the detection of local vehicles in Bangladesh. In this experiment, we have used 7 classes (Bus, CNG, Leguna, Pickup, Rickshaw, Thelagari, Van). We trained our model with our own dataset and our CNN model acquired accuracy is 96%.