Toward interpretable anomaly detection for autonomous vehicles with denoising variational transformer

Min, Haigen; Lei, Xiaoping; Wu, Xia; Fang, Yukun; Chen, Shixiang; Wang, Wuqi; Zhao, Xiangmo

doi:10.1016/j.engappai.2023.107601

Cited by 25 publications

(4 citation statements)

References 27 publications

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“…The optimal cluster head is selected by evaluating the relationship between the random number and the attribute and the weight parameters are optimized based on the GA and the simulated incubation algorithm. Reference [ 52 ] proposed a low-power routing protocol, where the node attribute is defined around the distance between the node and the base station and the remaining power factor. Reference [ 53 ] proposed a hybrid heterogeneous clustering routing protocol based on fuzzy logic theory and ant colony optimization.…”

Section: Related Workmentioning

confidence: 99%

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications

Gökhan Nalbant,

Alsuhibany,

Hassan Alshehri

et al. 2024

PLoS ONE

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Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm.

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Section: Related Workmentioning

confidence: 99%

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications

Gökhan Nalbant,

Alsuhibany,

Hassan Alshehri

et al. 2024

PLoS ONE

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“…Until all of the pixels in the search image have been processed, the algorithm keeps analyzing it. We use the normalized cross-correlation coefficient to evaluate the similarity [65], [66], as shown in Eq. ( 18).…”

Section: E Vehicle Detectionmentioning

confidence: 99%

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Yusuf,

Hanzla,

Rahman

et al. 2024

IEEE Access

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Systems must be capable of detecting and tracking autonomous vehicles for intelligent management and control of transportation. Even though several methods are used to create intelligent systems for traffic monitoring, this article explains how to detect and track vehicles using pixel labeling and particle filter algorithms. We suggested a novel technique that segments the image using image segmentation to retrieve the foreground objects. We have divided our proposed model into the following steps: at first, geo-referencing is used to find the exact location; secondly, the images are denoised by using preprocessing; image segmentation is used to separate the background from the foreground; multiple objection detection is performed using the random forest to classify different objects; vehicles are detected through a method called template matching; after this, the vehicles are counted using histogram of oriented gradients (HOG); and after counting, the tracking of vehicles is obtained using particle filter; lastly, the trajectories are predicted by comparing the rectangular centroid of each car against the frame number and using it as a time stamp reference, the last match that the tracking algorithm obtained for each vehicle was recorded and used to estimate the trajectories. Our model outperforms current traffic monitoring approaches in terms of detection and tracking accuracy by 0.87 and 0.92, respectively using the Aerial Car dataset and 0.84 and 0.88, respectively, using the AU-AIR datasets. Vehicle recognition, traffic density detection, traffic flow analysis, and pedestrian route generation are all possible uses for the proposed system. It can transform traffic management and improve overall road safety due to its powerful algorithms and cutting-edge technologies. The system's adaptability makes it a significant asset in modern transportation, from optimizing signal timing to improving pedestrian navigation.INDEX TERMS geo-referencing, random forest, particle filter, vehicle detection, tracking, and trajectories.

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“…The high-resolution representations are recovered using encoder-decoder models. HRNet, on the other hand, preserves high-resolution representations by occasionally transferring information (Liu H. et al, 2022;Liu Y. et al, 2022;Liu D. et al, 2022;Min et al, 2024;Zhao L. et al, 2024;Zhao X. et al, 2024) between resolutions and connecting the highto-low convolutions streams in parallel. It is therefore used as the basis for future models and improves segmentation accuracy (Liu et al, 2021;Fu et al, 2023) The UNet model is made up of several convolutional blocks for feature extraction and up sampling blocks (Yu et al, 2021;Hou et al, 2023a,b;Xiao et al, 2023a,b,c) for segmentation.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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Toward interpretable anomaly detection for autonomous vehicles with denoising variational transformer

Cited by 25 publications

References 27 publications

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Remote intelligent perception system for multi-object detection

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