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1. AROD: Adaptive Real-Time Object Detection Based on Pixel Motion Speed

   Liu, Yu; Kang, Kyoung-Don (October 2024, IEEE Vehicular Technology Conference)

   Real-time object detection is essential for AI-based intelligent traffic management. However, growing complexities of deep learning models for object detection cause increased latency and resource requirements. To tackle the challenge, we introduce a new approach, named AROD (Adaptive Real-Time Object Detection), that infers the pixel motion speed in continuous traffic video frames and skips redundant frames when the pixel velocity is low. Thereby, AROD aims to significantly enhance the efficiency and scalability, sustaining the accuracy of object detection. Our evaluation using real-world traffic videos reveals that our method for pixel velocity inference via lightweight deep learning reduces the RMSE (Root Mean Square Error) by up to two orders of magnitude compared to state-of-the-art approaches. AROD improves the frame processing rate of YOLOv5, SSD, and EfficientDet by approximately 32-61\%, 110-174\%, and 120-213\%, respectively. AROD considerably enhances scalability by supporting real-time object detection for up to three concurrent traffic video streams on a commodity machine. Moreover, AROD demonstrates its generalizability by supporting competitive accuracy in object detection for a separate traffic video that was fully hidden during training. 

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2. A Real-time Machine Learning-based GPS Spoofing Solution for Location-dependent UAV Applications

   https://doi.org/10.1109/eIT57321.2023.10187344  

   M. Nayfeh; J. Price; M. Alkhatib; K. Al Shamaileh; N. Kaabouch; V. Devabhaktuni (July 2023, IEEE International Conference on Electro Information Technology)

   In this paper, a three-class machine learning (ML) model is implemented on an unmanned aerial vehicle (UAV) with a Raspberry Pi processor for classifying two global positioning system (GPS) spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing a dataset collected in a previous work. This dataset conveys GPS-specific features, including location information. Models evaluations are carried out using the detection rate, F-score, false alarm rate, and misdetection rate, which all showed an acceptable performance. Then, the optimum model is loaded to the processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportations are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the developed model. 

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3. Boundless: Generating photorealistic synthetic data for object detection in urban streetscapes

   Turkcan, M-K; Li, Y; Zang, C; Ghaderi, J; Zussman, G; Kostic, Z (September 2024, arxiv)

   We introduce Boundless, a photo-realistic synthetic data generation system for enabling highly accurate object detection in dense urban streetscapes. Boundless can replace massive real-world data collection and manual groundtruth object annotation (labeling) with an automated and configurable process. Boundless is based on the Unreal Engine 5 (UE5) City Sample project with improvements enabling accurate collection of 3D bounding boxes across different lighting and scene variability conditions. We evaluate the performance of object detection models trained on the dataset generated by Boundless when used for inference on a real-world dataset acquired from medium-altitude cameras. We compare the performance of the Boundless-trained model against the CARLA-trained model and observe an improvement of 7.8 mAP. The results we achieved support the premise that synthetic data generation is a credible methodology for training/fine-tuning scalable object detection models for urban scenes. 

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4. Distributed Edge Cloud R-CNN for Real Time Object Detection

   Herrera, Joshua; Demir, Mevlut; Prevost, John; Rad, Paul (July 2018, World Automation Congress proceedings)

   Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks but these performance improvements only focus on a single contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN by splitting the model into components and dynamically distributing these components in the cloud for optimal performance for real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform encompasses cloud servers and edge embedded devices for real-time object detection on video streams. 

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5. Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

   Yu, Shuyang; Hong, Junyuan; Wang, Haotao; Wang, Zhangyang; Zhou, Jiayu (April 2023, International Conference on Learning Representations (ICLR))

   Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection performance for centralized training, and however, OoD detection in federated learning (FL) is largely overlooked, even though many security sensitive applications such as autonomous driving and voice recognition authorization are commonly trained using FL for data privacy concerns. The main challenge that prevents previous state-of-the-art OoD detection methods from being incorporated to FL is that they require large amount of real OoD samples. However, in real-world scenarios, such large-scale OoD training data can be costly or even infeasible to obtain, especially for resource-limited local devices. On the other hand, a notorious challenge in FL is data heterogeneity where each client collects non-identically and independently distributed (non-iid) data. We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL. The key is that for each client, non-iid data from other clients (unseen external classes) can serve as an alternative to real OoD samples. Specifically, we propose a novel Federated Out-of-Distribution Synthesizer (FOSTER), which learns a class-conditional generator to synthesize virtual external-class OoD samples, and maintains data confidentiality and communication efficiency required by FL. Experimental results show that our method outperforms the state-of-the-art by 2.49%, 2.88%, 1.42% AUROC, and 0.01%, 0.89%, 1.74% ID accuracy, on CIFAR-10, CIFAR-100, and STL10, respectively. 

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