Small object detection for UAV using bio-inspired spiking neural network

WANG Si-qi; LIU Jiang; SRIGRAROM Sutthiphong; XIANG Cheng; KHOO Boo Cheong; CAI Bai-gen

doi:10.19818/j.cnki.1671-1637.2026.156

Volume 26 Issue 3

Mar. 2026

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WANG Si-qi, LIU Jiang, SRIGRAROM Sutthiphong, XIANG Cheng, KHOO Boo Cheong, CAI Bai-gen. Small object detection for UAV using bio-inspired spiking neural network[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 261-275. doi: 10.19818/j.cnki.1671-1637.2026.156

Citation:

WANG Si-qi, LIU Jiang, SRIGRAROM Sutthiphong, XIANG Cheng, KHOO Boo Cheong, CAI Bai-gen. Small object detection for UAV using bio-inspired spiking neural network[J]. Journal of Traffic and Transportation Engineering, 2026, 26(3): 261-275. doi: 10.19818/j.cnki.1671-1637.2026.156

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Small object detection for UAV using bio-inspired spiking neural network

doi: 10.19818/j.cnki.1671-1637.2026.156

1.
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China
3.
Department of Mechanical Engineering, National University of Singapore, Singapore 117411, Singapore
4.
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077, Singapore

Funds:

National Natural Science Foundation of China U2268206

Science and Technology Innovation Program of Xiong'an New Area 2025XAGG0058

Independent Research Project of State Key Laboratory of Advanced Rail Autonomous Operation (Beijing Jiaotong University) RAO2025ZD001

More Information

Corresponding author: LIU Jiang, professor, PhD, E-mail: jingliu@bjtu.edu.cn
Received Date: 2025-08-26
Accepted Date: 2026-01-23
Rev Recd Date: 2025-11-29
Publish Date: 2026-03-28

Abstract

Abstract

To address the problem that small object detection methods for unmanned aerial vehicles struggle to achieve an effective trade-off between detection accuracy and computational complexity, which hinders practical deployment, a SpikeSOD model based on the bio-inspired spiking neural network (SNN) framework was proposed for small object detection of unmanned aerial vehicles. The model scheme was improved based on the YOLOv8 object detection model. A leaky integrate and multi fire (LIMF) neuron inspired by the multi-synaptic structure of biological neurons was introduced to reduce the spike quantization error and alleviate the small object information loss problem exacerbated by the sparsity of the SNN model. A lightweight spiking feature enhancement module inspired by the lateral inhibition mechanism of biological visual neurons was used to enhance the perception ability of the backbone network for the local information of small objects and their surrounding environment. A spiking feature fusion module was adopted to enhance the multi-scale feature fusion and complementary representation abilities of the neck network. The proposed model was validated on the unmanned aerial vehicle dataset VisDrone-DET2019. The results indicate that compared with the baseline YOLOv8n model, the proposed SpikeSOD model increases the mean average precision by 22.7%, reduces the parameter amount by 16.7%, and decreases the energy consumption by 12.3 mJ, among which the improvement in small object detection performance is particularly significant. The designed LIMF neuron increases the mean average precision by 8.8% compared with the most competitive neuron and effectively alleviates the limitations of traditional SNN models in small object information processing. Compared with existing object detection models, the SpikeSOD model achieves an effective balance among three key indicators, i.e., detection accuracy, lightweight design, and low power consumption, and has significant feasibility and application potential for practical deployment on unmanned aerial vehicle platforms.
- low-altitude traffic,
- unmanned aerial vehicle,
- spiking neural network,
- small object detection,
- spiking neuron,
- feature enhancement,
- multi-scale feature fusion

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References(38)

References

[1]	WEI W, CHENG Y, HE J F, et al. A review of small object detection based on deep learning[J]. Neural Computing and Applications, 2024, 36(12): 6283-6303. doi: 10.1007/s00521-024-09422-6
[2]	YU Lei, SHI Bo-xin, WANG Wei, et al. Neuromorphic-enabled visual enhancement: Principles, methods and recent advances[J]. Journal of Image and Graphics, 2025, 30(6): 1593-1615.
[3]	NEFTCI E O, MOSTAFA H, ZENKE F. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[J]. IEEE Signal Processing Magazine, 2019, 36(6): 51-63. doi: 10.1109/MSP.2019.2931595
[4]	CHENG G, YUAN X, YAO X W, et al. Towards large-scale small object detection: Survey and benchmarks[J]. IEEE Transactions on Pattern Analysis and Machine Intel-ligence, 2023, 45(11): 13467-13488.
[5]	JIANG Shi-xin, ZOU Xiao-xue, YANG Jian-xi, et al. Concrete bridge crack detection method based on improved YOLOv8s in complex backgrounds[J]. Journal of Traffic and Trans-portation Engineering, 2024, 24(6): 135-147.
[6]	LIU K, FU Z H, JIN S, et al. ESOD: Efficient small object detection on high-resolution images[J]. IEEE Transactions on Image Processing, 2025, 34: 183-195. doi: 10.1109/TIP.2024.3501853
[7]	ZOPH B, CUBUK E D, GHIASI G, et al. Learning data augmentation strategies for object detection[C]//Springer. Computer Vision-ECCV 2020. Berlin: Springer, 2020: 566-583. .
[8]	CHEN Z D, JI H B, ZHANG Y Q, et al. High-resolution feature pyramid network for small object detection on drone view[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(1): 475-489. doi: 10.1109/TCSVT.2023.3286896
[9]	CHEN Ting, YAO Da-chun, GAO Tao, et al. A fused network based on PReNet and YOLOv4 for traffic object detection in rainy environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 225-237. doi: 10.19818/j.cnki.1671-1637.2022.03.018
[10]	ZHOU S L, ZHOU H J, QIAN L. A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images[J]. Scientific Reports, 2025, 15: 9255. doi: 10.1038/s41598-025-92344-7
[11]	FAN Q S, LI Y T, DEVECI M, et al. LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle[J]. Information Sciences, 2025, 686: 121366. doi: 10.1016/j.ins.2024.121366
[12]	WANG H Z, LIU J H, ZHAO J, et al. Precision and speed: LSOD-YOLO for lightweight small object detection[J]. Expert Systems with Applications, 2025, 269: 126440. doi: 10.1016/j.eswa.2025.126440
[13]	LIU Yi-nuo, ZHANG Qi, WANG Rong, et al. Improved YOLOv7 method for aerial small target detection in aerial photography[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(7): 2506-2512.
[14]	WU Y M, MU X F, SHI H, et al. An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion[J]. Scientific Reports, 2025, 15: 16214. doi: 10.1038/s41598-025-00239-4
[15]	WANG R P, LIN C, LI Y J. RPLFDet: A lightweight small object detection network for UAV aerial images with rational preservation of low-level features[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 5013514.
[16]	WANG Ying-long, SUN Bei, DING Bing, et al. BG-YOLO: A low-altitude slow-moving small UAV targets detection method in complex large field of view[J]. Chinese Journal of Scientific Instrument, 2025, 46(2): 255-266.
[17]	SHEN C C, QIAN J B, WANG C, et al. Dynamic sensing and correlation loss detector for small object detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5627212.
[18]	ZHU X Z, SU W J, LU L W, et al. Deformable DETR: Deformable transformers for end-to-end object detection[J/OL]. ICLR, 2020, https://arxiv.org/abs/2010.04159.
[19]	ZHAO Y A, LV W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[C]//IEEE. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2024: 16965-16974.
[20]	ZHANG P R, ZHANG G X, YANG K H. APNet: Accurate positioning deformable convolution for UAV image object detection[J]. IEEE Latin America Transactions, 2024, 22(4): 304-311. doi: 10.1109/TLA.2024.10472961
[21]	QI Y S, CAO H W. SDFA-net: Synergistic dynamic fusion architecture with deformable attention for UAV small target detection[J]. IEEE Access, 2025, 13: 110636-110647. doi: 10.1109/ACCESS.2025.3578737
[22]	KONG Y N, SHANG X F, JIA S J. Drone-DETR: Efficient small object detection for remote sensing image using enhanced RT-DETR model[J]. Sensors, 2024, 24(17): 5496. doi: 10.3390/s24175496
[23]	LIANG C, GAO S B, HE L T, et al. Biological vision inspired context-awareness network for various non-generic object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2025, 35(7): 6726-6739. doi: 10.1109/TCSVT.2025.3540495
[24]	PANG X T, LIN C, LI F Z, et al. Bio-inspired XYW parallel pathway edge detection network[J]. Expert Systems with Applications, 2024, 237: 121649. doi: 10.1016/j.eswa.2023.121649
[25]	HUANG K J, LIN C, PENG J S. Bio-inspired visual mecha-nism lightweight network for edge detection[J]. Signal, Image and Video Processing, 2025, 19(6): 450. doi: 10.1007/s11760-025-04050-6
[26]	IABONI C, ABICHANDANI P. Event-based spiking neural networks for object detection: A review of datasets, architec-tures, learning rules, and implementation[J]. IEEE Access, 2024, 12: 180532-180596. doi: 10.1109/ACCESS.2024.3479968
[27]	KIM S, PARK S, NA B, et al. Spiking-YOLO: Spiking neural network for energy-efficient object detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 11270-11277. doi: 10.1609/aaai.v34i07.6787
[28]	SU Q Y, CHOU Y H, HU Y F, et al. Deep directly-trained spiking neural networks for object detection[C]//IEEE. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2023: 6532-6542.
[29]	FAN Y M, LIU C S, LI M Y, et al. SpikeDet: Better firing patterns for accurate and energy-efficient object detection with spiking neural networks[J/OL]. ICLR, 2025, https://arxiv.org/abs/2501.15151.
[30]	LUO X H, YAO M, CHOU Y H, et al. Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection[C]// Springer. Computer Vision-ECCV 2024. Berlin: Springer, 2024: 253-272.
[31]	YAO M, HU J K, HU T X, et al. Spike-driven transformer V2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips[J/OL]. ICLR, 2024, https://arxiv.org/abs/2404.03663.
[32]	HUO Y S, GE H W, TANG G Z, et al. Spiking Trans-YOLO: A range-adaptive energy-efficient bridge between YOLO and Transformer[J]. Neurocomputing, 2025, 645: 130407. doi: 10.1016/j.neucom.2025.130407
[33]	DU D W, ZHU P F, WEN L Y, et al. VisDrone-DET2019: The vision meets drone object detection in image challenge results[C]//IEEE. 2019 IEEE/CVF International Confe-rence on Computer Vision Workshops (ICCVW). New York: IEEE, 2019: 213-226.
[34]	SONG Yao-lian, WANG Can, LI Da-yan, et al. UAV small target detection algorithm based on improved YOLOv5s[J]. Journal of Zhejiang University (Engineering Science), 2024, 58(12): 2417-2426.
[35]	ZHONG H, ZHANG Y, SHI Z G, et al. PS-YOLO: A lighter and faster network for UAV object detection[J]. Remote Sensing, 2025, 17(9): 1641. doi: 10.3390/rs17091641
[36]	LI Q P, ZHANG Y X, FANG L Y, et al. DREB-net: Dual-stream restoration embedding blur-feature fusion network for high-mobility UAV object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5621218.
[37]	WANG J Y, GAO J Y, ZHANG B. A small object detection model in aerial images based on CPDD-YOLOv8[J]. Scien-tific Reports, 2025, 15: 770. doi: 10.1038/s41598-024-84938-4
[38]	NIAN Z Y, YANG W Z, CHEN H. AEFFNet: Attention enhanced feature fusion network for small object detection in UAV imagery[J]. IEEE Access, 2025, 13: 26494-26505. doi: 10.1109/ACCESS.2025.3538873

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Small object detection for UAV using bio-inspired spiking neural network

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