Mr. Haohao Li Profile

Haohao Li

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Proceedings Article | 21 December 2023 Paper

Algorithm for urban air mobility object recognition based on spiking neural networks

Jingyi Qu, Haohao Li

Proceedings Volume 12970, 1297049 (2023) https://doi.org/10.1117/12.3012474

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In recent years, a large number of intelligent unmanned aerial vehicles (UAVs) have emerged in people's vision, leading to the development of a new safe and efficient urban air mobility (UAM) system, which is suitable for both manned and cargo scenarios. Driven by the market, this service is expected to become a new trend in the development of civil aviation. The rapid development of deep learning has brought prospects to urban air traffic, but the increasing data volume and model complexity pose challenges in terms of resource consumption and model deployment. A method of applying spiking neural network (SNN) algorithms in UAM is proposed in this paper, utilizing spike-based transmission to significantly reduce energy consumption compared to deep learning algorithms. To address the issue of missing urban air traffic data sets, we build a complex urban air traffic data set comprising 10 categories such as pedestrians, vehicles, buildings, traffic signals, billboards, garbage bins, unmanned aerial vehicles, hot air balloons, cats, and dogs. In the network model, we introduce the biological neurons such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), achieving the highest accuracy of 81.937% and 82.772%, respectively. Based on the LIF neuron, we propose the Self-Learning Leaky Integrate-and-Fire (SLLIF) neuron, which autonomously learns stimulus-input ratio relationships to better align with the brain's automatic optimization mechanism, achieving a recognition accuracy of 85.484%. Furthermore, we re-evaluate the selection of the hyper parameter time steps and choose suitable values for different neural network models to reduce resource consumption while maximizing accuracy.

Proceedings Article | 9 October 2023 Paper

SimAM-based optimization algorithm for small target detection

Jing-yi Qu, Yun-long Li, Hao-hao Li, Shan-liang Liu

Proceedings Volume 12791, 127910Q (2023) https://doi.org/10.1117/12.3005083

KEYWORDS: Small targets, Target detection, Neurons, Detection and tracking algorithms, Mathematical optimization, Deep learning, Performance modeling, Matrices

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With the development of deep learning, small target detection application scenarios are gradually becoming more widespread, and higher demands are made on small target detection accuracy. The attention mechanism is a simple and convenient way to improve target detection accuracy. In order to improve the detection performance of the attention mechanism for small targets, a SimAM-based attention method for small target detection is proposed by us, which changes the activation method of SimAM and add hyperparameters to improve the detection effect of small targets. The experimental results show that the algorithm has good detection performance.

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