Object detection is a crucial task in the field of autonomous vehicles. To train the object detection models, data such as RGB data is collected either offline or online during the operation of the cars, and this data is used to train the models. However, in supervised learning approaches, which have superior performance, the data needs to be annotated.
Active learning is an approach that aims to reduce the amount of annotated data required. It achieves this by selecting the most informative information from the unannotated data pool. Unfortunately, most active learning approaches for object detection assume that the data is already gathered and located in a central entity (offline). These approaches overlook the consumption of networking resources that occur when transmitting the data in the online data gethering.
In this thesis, the student will have the chance to enable online data collection in a multi-agent system (e.g., vehicular system), while considering the available network resources. The objective is to develop an active learning approach that optimizes the utilization of the network resources while maintaining high object detection performance. This would involve selecting the most informative data for annotation and training, taking into account the limitations of the available network resources.
The goal is to enhance the efficiency and effectiveness of object detection in autonomous vehicles by developing an active learning approach that leverages the available network resources without compromising the performance of the models.
This thesis is open for master/bachelor students, and the main tasks are as follows:
• Explore relevant existing related literature.
• Formalizing the problem and modeling the solution and
• Setting up a development environment.
• Evaluation of the proposed solution in comparison to the existing state-of-the-art approaches.
It is required to have:
• Motivated and individual working style
• Very good programming skills in Python
• Very good machine learning / Deep learning knowledge
 Yu, Weiping, et al. "Consistency-based active learning for object detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
 Choi, Jiwoong, et al. "Active learning for deep object detection via probabilistic modeling." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.
 Feng, Di, et al. "A review and comparative study on probabilistic object detection in autonomous driving." IEEE Transactions on Intelligent Transportation Systems 23.8 (2021): 9961-9980.