Abstract:
In self-driving technologies the system perceived the environment without human intervention in
which system can detect different obstacles and make decisions for smart transportation. In this
studies we adapt and design different technique for detecting and recognize an objects which
used different components like data processing, noise removal, input resize, input vector
preparation, feature extraction, classification and regression problems. However, in the current
research, the performance of the detector is not reach matured. And they used fully connected
layers in detection networks for the detector models. Due to this problem the performance in the
detection networks is not satisfied and doesn't extract many features. We developed a new model
in detection networks using convolutional neural networks and extracted different level of
features which helps the model to extract more usable information to the classification and
regression problems in the detector. In the proposed model we used 3 layers of fully
convolutional neural networks and two fully connected layers to develop the model. In the
experiment, we have evaluate both the localization and classification mAP of the networks. And,
we obtained 84% mAP model performance. And also, we evaluate the quantitative and
qualitative results for the networks for each categories in the input data. Thus, our model, we
detected more objects that doesn't detect in the previous works.
