Camera Perspective Transformation to Bird's Eye View via Spatial Transformer Model for Road Intersection Monitoring

Road intersection monitoring and control research often utilize bird's eye view (BEV) simulators. In real traffic settings, achieving a BEV akin to that in a simulator necessitates the deployment of drones or specific sensor mounting, which is neither feasible nor practical. Consequently, traffic intersection management remains confined to simulation environments given these constraints. In this paper, we address the gap between simulated environments and real-world implementation by introducing a novel deep-learning model that converts a single camera's perspective of a road intersection into a BEV. We created a simulation environment that closely resembles a real-world traffic junction. The proposed model transforms the vehicles into BEV images, facilitating road intersection monitoring and control model processing. Inspired by image transformation techniques, we propose a Spatial-Transformer Double Decoder-UNet (SDD-UNet) model that aims to eliminate the transformed image distortions. In addition, the model accurately estimates the vehicle's positions and enables the direct application of simulation-trained models in real-world contexts. SDD-UNet model achieves an average dice similarity coefficient (DSC) above 95% which is 40% better than the original UNet model. The mean absolute error (MAE) is 0.102 and the centroid of the predicted mask is 0.14 meters displaced, on average, indicating high accuracy.

Cloud-based Federated Learning Framework for MRI Segmentation

In contemporary rural healthcare settings, the principal challenge in diagnosing brain images is the scarcity of available data, given that most of the existing deep learning models demand extensive training data to optimize their performance, necessitating centralized processing methods that potentially compromise data privacy. This paper proposes a novel framework tailored for brain tissue segmentation in rural healthcare facilities. The framework employs a deep reinforcement learning (DRL) environment in tandem with a refinement model (RM) deployed locally at rural healthcare sites. The proposed DRL model has a reduced parameter count and practicality for implementation across distributed rural sites. To uphold data privacy and enhance model generalization without transgressing privacy constraints, we employ federated learning (FL) for cooperative model training. We demonstrate the efficacy of our approach by training the network with a limited data set and observing a substantial performance enhancement, mitigating inaccuracies and irregularities in segmentation across diverse sites. Remarkably, the DRL model attains an accuracy of up to 80%, surpassing the capabilities of conventional convolutional neural networks when confronted with data insufficiency. Incorporating our RM results in an additional accuracy improvement of at least 10%, while FL contributes to a further accuracy enhancement of up to 5%. Collectively, the framework achieves an average 92% accuracy rate within rural healthcare settings characterized by data constraints.

iDriveSense: Dynamic Route Planning Involving Roads Quality Information

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.