Radar target recognition software

Object recognition is an important computer vision and machine learning problem. A particular case is automatic target recognition (ATR) on radar images. In the project, our team has developed a custom classification algorithm based on two different tools.

We have been working with MSTAR dataset. This is a public dataset containing ten classes of vehicles with different orientations with 0.3mx0.3m.

Target recognition. Classes of vehicles with different orientations

Fig.1. MSTAR dataset with 10 vehicles

We have built two pipelines: SVM-based and CNN-based. For the former, we have constructed several feature sets, optimized parameters of the classifier using K-fold cross-validation and conducted comparative experiments.

Target recognition system. SVM-based conveyor

Fig. 2. SVM application steps

The highest recognition accuracy of 94.1% was achieved using the fusion of vertical and horizontal image profiles. Such feature representation was around 20x times more compact than Haralick features, while provided better performance.

The second applied tool was CNN. Below is the proposed architecture:

Target recognition system. CNN-based conveyor

Fig. 3. CNN architecture

Created CNN consists of 3 convolutional layers and 4 fully connected layers. Dropout layers were applied for careful control of overfitting.

Proposed CNN-based solution provided the state-of-the-art level of recognition accuracy.

CNN-based solution - high level of recognition accuracy

Fig. 4. Classification results for CNN (left – confusion matrix, right – total accuracies)

The maximum achieved accuracy is 99.5%, which is the best result in the community for the public MSTAR database!

Object Recognition in Radar Images