# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 5 Deep Convolutional Neural Networks  
We have two scenarios for target classification based on radar frontal images. First, we classified six classes using a DCNN. The six classes were sedan, van, truck, pole, tree, and human. In the second scenario, we classified 3 classes, vehicles (sedan, van, truck), humans, and clutter (pole and tree). To classify targets, we used deep neural networks that surpass previous classifiers, as they are proven to be excellent at abstraction and generalization for radar target classification [17; 18; 19; 20]. The DCNN extracts images feature through the convolution of those images which leads to high performance in target recognition. In this paper, road target detection and classification method based on DCNN is suggested. A DCNN is mainly consisting of multiple convolution layers, pooling layers, and fully connected layers, and finally uses SoftMax to classify and output. This architecture on the types and numbers of layers is varied depending on the size and characteristics of the dataset. Convolutional layers are the first layers of the DCNN architecture, which extract features of images through convolutional filters. The layers located between each of the two convolutions layers are pooling layers that are responsible for simplifying the data by reducing its dimensionality. This minimizes the time required for training and helps to restrain the problem of overfitting. The last layers of the deep learning network are the fully connected layers. In these layers, every neuron in the first is connected to every neuron in the next. The process during this network training looks at what features most accurately describe the specific classes, and the output generates the probabilities that are organized according to each class. For each objective, a DCNN structure was optimized heuristically. The structure of DCNN is divided into three main parts. The first part passes the input into a set of convolution layers with a filter size of 2 \(\times\) 2 and then into the max-pooling layer. The second part is the same as the first except for the increased number of filters in the convolution layers with the max-pooling layer at the end. The last part is fully connected layers with dropout layers. In the fully connected layer, each neuron is connected to all neurons in the previous layer. The ReLu layer is applied after every convolutional and fully connected layer to not activate all the neurons at the same time. Since only a certain number of neurons are activated, the ReLu function is computationally efficient. The dropout layer is applied before the first and the second are fully connected to reduce overfitting and improve the generalization of deep neural networks. The designed network is presented in Figure 7.  
Although the measurements are conducted in a controlled environment, some data are quite noisy due to the low SNR. For example, measured data from targets such as poles, trees, and humans could not be fully used for training when images become very noisy. Thus, the class with the smallest data samples becomes the reference. Finally, each class has 2047 data points; a total of 10,235 radar images are ava  
Figure 7: The structure of DCNN. construction of the data set, we cropped the frontal image to a size of 40 \(\times\) 40 pixels, with the peak power in the center. We also normalized the image values from 0 to 255. The data are divided into a training set (80%), a testing set (10%), and a validation set (10%). For the training, the stochastic gradient descent with momentum optimizer was used with an initial learning rate of 0.0004 and a momentum of 0.9. To improve the training, the training loss is dropped every ten epochs using exponential decay with a decay rate of 0.96. The mini-batch size was set to 32, and the max epoch was set at 1000. To prevent overfitting, an early stop was set to monitor the validation loss and stop after five epochs if the losses were not dropping, at which point training stops. We used a five-fold validation method by shuffling the dataset.