Structural defects in civil infrastructure, such as highways, roads, bridges, and dams, can severely impact their reliability and safety. Manual inspection of such infrastructure is labor-intensive and costly, creatin...
Structural defects in civil infrastructure, such as highways, roads, bridges, and dams, can severely impact their reliability and safety. Manual inspection of such infrastructure is labor-intensive and costly, creating the demand for automated damage-tracking systems. Data-driven techniques, such as machine learning, and statistical methods have been utilized in a remarkable number of Structural Health Monitoring (SHM) applications. However, real-time and high-accuracy concrete crack detection poses severe challenges for deployment on embedded systems and mobile robotic platforms, due to their computational constraints, low-latency requirements, and the robustness of model predictions in field deployments. This work proposes a robust and low-latency transformer-based deep-crack segmentation model that leverages both Red Green Blue (RGB) and Hyper Spectral (HYP) data for crack detection. The model is optimized for deployment on resource-constrained embedded systems, enabling structural health monitoring on robotic platforms, and studies the aleatoric and epistemic uncertainties of the estimated crack maps. The final model is deployed on one of the most popular robotic embedded computation platforms such as the NVIDIA Jetson Xavier NX, achieving state-of-the-art performance with an accuracy of 99.54% at an inference time of only 7.41ms using the RGB camera only. The proposed approach provides a promising solution for automating laborious SHM applications such as inspecting critical infrastructure.
This paper discusses the possibility of the new QAVIS software technology to measure or estimate wind wave and swell signals in the coastal zones of the oceans. The developed technology is based on the analysis of vid...
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ISBN:
(数字)9781665427920
ISBN:
(纸本)9781665427920
This paper discusses the possibility of the new QAVIS software technology to measure or estimate wind wave and swell signals in the coastal zones of the oceans. The developed technology is based on the analysis of video streams from coastal Internet cameras. QAVIS processes data from screen graphic memory. This allows processing streaming video in real-time indefinitely. The QAVIS accuracy of wave processes measurements inferior to that of professional oceanographic instruments, but is sufficient for many applications. A brief description of the technology is presented, and examples of the analysis of synchronous continuous 34-day observations of wind waves and swell in the United States, Mexico, and Costa Rica coastal zones are given. The QAVIS-derived results are compared with satellite SAR images and seismic data.
High-performance light detection and ranging (LiDAR) modules are highly demanded for advanced driving assistance system and autonomous driving. The high power level of emitter required for long-distance ranging is one...
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ISBN:
(数字)9781665409810
ISBN:
(纸本)9781665409810
High-performance light detection and ranging (LiDAR) modules are highly demanded for advanced driving assistance system and autonomous driving. The high power level of emitter required for long-distance ranging is one of the key issues to be addressed to realize commercially affordable vehicle LiDAR. In this work, for the first time, we demonstrate a low-cost real-time LiDAR module by using CMOS-fabricated 64x128-pixel single-photon avalanche diode (SPAD) array, a 940-nm photonic-crystal surface-emitting laser (PCSEL), and a FPGA card for control and signal processing. Thanks to the high-sensitivity of SPAD and small divergence angle of PCSEL, a 60-m 3-D imager has been realized with a low laser peak power of similar to 0.5 W. The ranging distance and frame rate are 60 m and 10 frames per second, respectively, for the target reflectivity of 10% - 90% and the sunlight condition of 0 - 50k lux. Our work reveals the great potential of these two intriguing devices for making low-cost high-performance vehicle LiDARs.
Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems ar...
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With the increasing popularity of electric bicycles, safety concerns regarding electric bicycle sheds have garnered public attention. In order to protect people’s life and property safety, an accurate and real-time f...
With the increasing popularity of electric bicycles, safety concerns regarding electric bicycle sheds have garnered public attention. In order to protect people’s life and property safety, an accurate and real-time fire monitoring system for electric bicycle shed is crucial. We proposed a YOLOv8 based fire monitoring system for electric bicycle shed. The system can be conveniently deployed on various end devices, computers, and IoT terminals. The proposed system uses deep learning technology to offer round-the-clock real-time monitoring, early fire detection, and automatic alarm triggering capabilities. The experimental results demonstrated that the proposed system can successfully monitor fire for electric bicycle. It is highly practical and can be widely used for fire monitoring and other similar applications.
Edge AI is a next-generation paradigm for crafting and running scores of real-time and rewarding applications and services for businesses and people. Edge AI is the powerful combination of two hugely popular domains o...
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In recent times, Federated Learning (FL) has played a vital role in real-timeapplications by collaboratively learning a shared model across massive end devices without exchanging local data. However, most of the exis...
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Augmented reality (AR)-based quality inspection (QI) has the potential to aid operators in industry by providing additional information and real-time feedback on their actions. To realise the full potential of AR, use...
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Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lig...
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ISBN:
(数字)9798350365474
ISBN:
(纸本)9798350365481
Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.
The integration of Industrial Internet of Things (IIoT) and time-Sensitive Networking (TSN) enables efficient and reliable real-time communication and control. Currently, one of the current research hotspots in the fi...
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