(Th3). Amira MIMOUNA

• Thesis topic : Exploring Data Fusion for Multi-Object Detection for Intelligent Transportation Systems using Deep Learning. 

• Thesis director :  Pr. Najoua Essoukri BEN AMARA and Pr. Abdelmalik TALEB-AHMED

• Co-supervisor : Dr. Anouar BEN KHALIFA and Dr. Ihsen ALOUANI. 

• Institution : ENISo, University of Sousse., Tunisia and Polytechnic University Hauts-De-France. 

• Defense date : May 25, 2021.

• Abstract : Building reliable environment perception systems is a crucial task for autonomous driving, especially in dense traffic areas. Researching in this field is evolving increasingly. However, we are at the beginning of a research pathway towards a future generation of intelligent transportation systems. In fact, challenging conditions in real-world driving circumstances, infrastructure monitoring, and accurate real-time system response, are the predominant concerns when developing such systems. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems have been mainly based on deep learning and the fusion of different modalities. In this context, firstly, we introduce OLIMP1 : A heterOgeneous MuLtimodal Dataset for Advanced EnvIronMent Perception . This is the first public, multimodal and synchronized dataset that includes Ultra Wide-Band (UWB) radar data, acoustic data, narrowband radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, including four categories: pedestrians, cyclists, cars and trams. The dataset presents various challenges related to dense urban traffic such as cluttered environments and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose, afterwards, a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research. 

In short range settings, UWB radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant Regions Of Interests (ROIs) within its signals. Therefore, we put froward a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar as a third contribution. Specifically, we implement a differential entropy analysis to detect ROIs. The obtained results show higher performance in terms of obstacle detection compared to state-of-theart techniques, as well as stable robustness even with low amplitude signals. 

Subsequently, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection as a deep learning-based approach. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection.

• Key words : Intelligent transportation systems; Public dataset; Multi-modality; Fusion; Object detection; UWB radar; Entropy; Segmentation; Deep learning; LSTM.

• Publications : This thesis led to the publication of the following papers : 

(C26). Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Abdelmalik Taleb-Ahmed, Atika Rivenq, Najoua Essoukri Ben Amara, LSTM-based system for multiple obstacle detection using ultra-wide band radar, In Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Volume 2, pp. 418-425, Vienna, Austria, 2021. DOI : 10.5220/0010386904180425 (Conf.Rank C) .

(J16). Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Najoua Essoukri Ben Amara, Atika Rivenq, Abdelmalik Taleb-Ahmed, Entropy-based Ultra-Wide Band radar signals Segmentation for Multi Obstacle Detection, IEEE Sensors Journal, Vol. 21, No. 6, pp. 8142-8149, March 2021. DOI : 10.1109/JSEN.2021.3050054. Quartile: Q1, IF=3.301.

(J5). Amira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara, OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception, Electronics, Volume 9, March 2020, 560. DOI: https://doi.org/10.3390/electronics9040560. Quartile: Q2, IF= 2.397.