A Long-Short Term Fusion Spiking Neural Network for Detecting Tiny Moving Targets in Dynamic Vision

LI Miao; ZHANG Heng; CHEN Nuo; SHI Yangsi; HE Shiman; AN Wei

doi:10.11999/JEIT250785

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LI Miao, ZHANG Heng, CHEN Nuo, SHI Yangsi, HE Shiman, AN Wei. A Long-Short Term Fusion Spiking Neural Network for Detecting Tiny Moving Targets in Dynamic Vision[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250785

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LI Miao, ZHANG Heng, CHEN Nuo, SHI Yangsi, HE Shiman, AN Wei. A Long-Short Term Fusion Spiking Neural Network for Detecting Tiny Moving Targets in Dynamic Vision[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250785

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A Long-Short Term Fusion Spiking Neural Network for Detecting Tiny Moving Targets in Dynamic Vision

doi: 10.11999/JEIT250785 cstr: 32379.14.JEIT250785

University of China, Tianjin 300300, China) (College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

Received Date: 2025-08-22
Accepted Date: 2026-02-09
Rev Recd Date: 2026-01-16

Available Online: 2026-03-01

Abstract

Abstract

Objective The long-distance electro-optical surveillance system is widely used in fields such as space debris monitoring and unauthorized drone flight warning. The targets in this system randomly appear, move rapidly, and due to the long detection distance, the form of the targets in the optical detector is very small, without obvious morphological texture features, belonging to tiny-motion targets. The traditional mechanism for sensing tiny-motion targets adopts the "image frame imaging + artificial neural network processing" approach, which is always accompanied by large amounts of data, high computing power, and high energy consumption, becoming a bottleneck restricting the lightweight of the system. In recent years, inspired by bionic perception and brain-like processing, "dynamic visual detection + brain-like processing" has become the frontier mechanism. The dynamic vision has the advantages of low redundancy and high temporal resolution, but the output data is no longer regular image frames, but sparse event streams. Therefore, new processing methods need to be studied. The spiking neural network is called the third-generation neural network, which has the characteristics of sparse connections and spiking representation, and has a natural compatibility with the asynchronous event triggering and bright-dark pulse output of the dynamic vision. However, the existing spiking neural network methods are mainly oriented towards targets with special shapes in fields such as autonomous driving, are difficult adapt to the tiny-motion targets in long-distance electro-optical surveillance system. To address the above problems, this paper designs a long-short-term fusion pulse neural network, providing dedicated algorithm support for the application of the dynamic vision in the detection of tiny-motion targets. Methods The proposed network architecture consists of four key components. Firstly, a short-term feature extraction module (SST, Spiking Swin Transformer) is designed to capture morphological the morphological expansion characteristic of tiny targets, focusing on spatiotemporal correlations between adjacent time steps and spatial domains. It integrates a spiking self-attention mechanism to adaptively enhance learning of irregular pixel correlations and temporal dependencies. Second, a long-term feature extraction module (SCL, spiking ConvLSTM) is designed to learn motion continuity, which is embedded in long-term temporal sequences. The longer the temporal domain, the richer the learnable features. The spiking ConvLSTM network is designed by mimicking the ANN-style ConvLSTM, capitalizing on the inherent advantages of spiking recurrent neural networks for temporal signal processing to emphasize autonomous long-term temporal information memorization capabilities. Thirdly, dual-path features from SST and SCL are combined via tensor alignment and additive integration, called as SFPN(Spiking Feature Pyramid Network). Adopting spiking pyramid operations to fuse cross-scale spatiotemporal features across network depths. Finally, tiny targets are extracted by detection head. Results and Discussions The proposed algorithm was validated using real dynamic vision data for drone detection. Test results demonstrate significant performance improvements based different metrics. Compared to methods based on short-term temporal features, the proposed method achieves about 1.3% increase in recall and about 0.9% boost in accuracy, enabling more precise detection of tiny moving targets. The F1-score analysis further reveals that the proposed approach improves recall rates by 1.3%, and it simultaneously reduces false alarms. This confirms that the dual-path spiking memory network for long-term feature extraction enhances the model's capability to discern subtle target characteristics. Specifically, the incorporation of long-term temporal features contributes to overall performance gains, allowing better discrimination between noise events and genuine tiny targets. Conclusions This paper addresses the problem of detecting tiny moving targets under dynamic vision and proposes a method based on long-short term fusion of spiking neural networks. Considering the morphological expansion characteristics and motion continuity of tiny targets, the paper designs the spiking Swin Transformer module and the spiking ConvLSTM module respectively, and fuses multi-scale dual-path features through the spiking pyramid module. By learning high-dimensional features within different time windows, it achieves in-depth mining and automatic learning of limited surface features. The performance advantages of the proposed method are verified in real d datas, with a recall rate of over 95%, outperforming comparison algorithms. Ablation experiments demonstrate the importance of using long-term domain feature neural networks and more time-domain data to improve the performance of tiny target detection. This method realizes the natural combination of sparse event streams from dynamic vision and spiking neural mechanisms, providing algorithmic support for the application of the "bionic detection + brain-like processing" new perception mode in long-distance electro-optical surveillance systems.
- Tiny target,
- Target detection,
- Spiking Neural Network,
- Dynamic Vision

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References(25)

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