A computer vision framework using Convolutional Neural Networks for airport-airside surveillance

Thai, Phat; Alam, Sameer; Lilith, Nimrod; Nguyen, Binh T.

doi:10.1016/j.trc.2022.103590

Cited by 21 publications

(9 citation statements)

References 19 publications

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“…Among them, artificial neural network algorithms have demonstrated impressive performance and have emerged as the prevailing technology, and they have further evolved into the next generation, namely deep neural networks (DNN) and CNN. [82][83][84][85] Ma et al revealed a MoS 2 -based ANN chip capable of achieving a digit recognition accuracy of over 97%. [86] Pauletto and his colleagues successfully applied ANNs to effectively predict the single and binary absorption of nimesulide and paracetamol on activated carbon.…”

Section: Neural Network Algorithmmentioning

confidence: 99%

Memristor‐Based Neuromorphic Chips

Duan,

Cao,

Gao

et al. 2024

Advanced Materials

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In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain‐like chips, which are known for their robust processing power and energy‐efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor‐based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse‐neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

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Section: Neural Network Algorithmmentioning

confidence: 99%

Memristor‐Based Neuromorphic Chips

Duan,

Cao,

Gao

et al. 2024

Advanced Materials

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“…In the back propagation stage, the objective function is employed to measure the deviation between the actual value of the output and the corresponding predicted value, and the weights and deviations are updated. CNNs have been developed rapidly, achieving unprecedented accuracy in target classification and recognition based on computer vision, [77][78][79][80] object detection, 81 and scene analysis. 82 This has successfully solved some issues in multiple research directions, including image classification, image semantic segmentation, video tracking, and speech analysis.…”

Section: Memristor-based Neural Networkmentioning

confidence: 99%

Memristor-based neural networks: a bridge from device to artificial intelligence

et al. 2023

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“…The AirNet model was developed in previous work 48 , and was specifically adapted to provide detection of aircraft and ground vehicles in airport environments. To detect small flying objects, we create a small version of AirNet, called SAirNet, as shown in Figure 5.…”

Section: Object Detectionmentioning

confidence: 99%

Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets

Thai

Alam

Lilith

et al. 2023

Preprint

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Digital towers using high-resolution cameras that cover a 360-degree view of airports have recently been applied as a solution for some airports by replacing conventional towers. Although many computer vision systems have been developed as tools to assist tower controllers, small flying object detection remains challenging due to their small dimensions and unpredictable trajectories. This paper proposes a novel computer vision framework to detect, track and recognize small flying objects, namely aircraft and drones, in an airport environment. The framework creates a new Convolutional Neural Network which adapts to the unique characteristics of small flying objects. It also exploits the spatial-temporal information, as well as post-processing, to improve the performance. The proposed framework is validated on an airport dataset and Drone-vs-Bird public dataset. The results show that the framework can not only perform object detection in real-time, but also surpass the performance of state-of-the-art models in both datasets by a large margin.

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A computer vision framework using Convolutional Neural Networks for airport-airside surveillance

Cited by 21 publications

References 19 publications

Memristor‐Based Neuromorphic Chips

Memristor‐Based Neuromorphic Chips

Memristor-based neural networks: a bridge from device to artificial intelligence

Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets

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