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1. Fast Hand Detection in Collaborative Learning Environments

   https://doi.org/10.1007/978-3-030-89128-2_43  

   Teeparthi, S.; Jatla, V.; Pattichis, M.S.; Celedón-Pattichis, S.; Carlos LópezLeiva, C. (October 2021, CAIP 2021: Computer Analysis of Images and Patterns)

   Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance. Our approach integrates object-detection, followed by time projections, clustering, and small region removal to provide effective hand detection over long videos. The hand detector achieved average precision (AP) of 72% at 0.5 intersection over union (IoU). The detection results were improved to 81% by using our optimized approach for data augmentation. The method runs at 4.7× the real-time with AP of 81% at 0.5 intersection over the union. Our method reduced the number of false-positive hand detections by 80% by improving IoU ratios from 0.2 to 0.5. The overall hand detection system runs at 4× real-time. 

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2. Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing

   https://doi.org/10.3390/fi14110316  

   Bai, Tianyu; Fu, Song; Yang, Qing (November 2022, Future Internet)

   With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing. 

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3. Hybrid Cyber-attack Detection in Photovoltaic Farms

   https://doi.org/10.1109/ECCE53617.2023.10362667  

   Zhang, Jinan; Ye, Jin; Song, Wenzhan; Lian, Jianming; Zhao, Dongbo; Yang, He (October 2023, IEEE Xplore)

   To address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment. 

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4. Sequential detection of common transient signals in high dimensional data stream

   https://doi.org/10.1002/nav.22034  

   Wu, Yanhong; Wu, Wei Biao (June 2022, Naval Research Logistics (NRL))

   Abstract Motivated from sequential detection of transient signals in high dimensional data stream, we first study the performance of EWMA and MA charts for detecting a transient signal in a single sequence in terms of the power of detection under the constraint of false detecting probability in the stationary state. Satisfactory approximations are given for the false detection probability and the power of detection. Comparison of EWMA, MA, and CUSUM charts shows that both charts are quite competitive. A multivariate EWMA procedure is considered by using the squared sum of individual EWMA processes and a fairly accurate approximation for the false detection probability is also given. To increase the power of detection, we use the Min‐δ procedure by truncating the estimated weak signals. Dow Jones 30 industrial stock prices are used for illustration. 

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5. Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

   https://doi.org/10.3390/s23146255  

   Ogunrinde, Isaac; Bernadin, Shonda (July 2023, Sensors)

   AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps. 

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