Mohamad Alhaddad

Murat Tulgaç, Enes Yüncü, Mohamad-Alhaddad et al.

In traffic management, it is a very important issue to shorten the response time by detecting the incidents (accident, vehicle breakdown, an object falling on the road, etc.) and informing the corresponding personnel. In this study, an anomaly detection framework for road junctions is proposed. The final judgment is based on the trajectories followed by the vehicles. Trajectory information is provided by vehicle detection and tracking algorithms on visual data streamed from a fisheye camera. Deep learning algorithms are used for vehicle detection, and Kalman Filter is used for tracking. To observe the trajectories more accurately, the detected vehicle coordinates are transferred to the bird's eye view coordinates using the lens distortion model prediction algorithm. The system determines whether there is an abnormality in trajectories by comparing historical trajectory data and instantaneous incoming data. The proposed system has achieved 84.6% success in vehicle detection and 96.8% success in abnormality detection on synthetic data. The system also works with a 97.3% success rate in detecting abnormalities on real data.

Amit Sahu, Noelia Vállez, Rosana Rodríguez-Bobada et al.

In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development process of the neural network model. After interpreting neural networks via the different metrics available in the NNDK, the developers were able to increase the NNs' accuracy, trust the developed networks, and make them more robust. In addition, we obtained a novel application-oriented technique to provide supporting evidence for an NN's classification result to the user. In the medical image classification use case, it was used to retrieve case images from the training dataset that were similar to the current patient's image and could therefore act as a support for the NN model's decision and aid doctors in interpreting the results.