Despite their well-known energy advantages, spiking neural networks (SNNs) still fall short of conventional artificial neural networks in detection accuracy for on-orbit remote sensing, limiting their use on power-constrained satellites. To address this gap, we present SpikeYOLO-C2TCA, an improved SNN detector built upon the SpikeYOLO backbone. The core of our method is a new attention module, SpikeTCA, which operates directly on membrane potentials. It combines three complementary descriptors—direction-aware pooling, multi-scale depthwise convolution, and temporal convolution—and fuses them through cross-temporal self-attention and a lightweight channel MLP to enhance feature discriminability and suppress background clutter. In addition, we introduce a SpikeC2fCIB module that employs a five-layer depthwise-separable bottleneck following a split–process–merge structure, leading to better feature reuse, gradient flow, and computational efficiency. On the RSOD dataset, SpikeYOLO-C2TCA reaches an mAP50 of 96.7% and an mAP50-95 of 63.7%, exceeding the SpikeYOLO baseline by 6.6 and 1.2 percentage points, respectively. Overall, SpikeYOLO-C2TCA delivers better detection accuracy than the baseline while consuming less energy, offering a practical lightweight solution for edge platforms such as on-orbit satellites and long-endurance unmanned aerial vehicles.
Yang, N.; Zhu, Q.; Wu, P.; Wang, Y.; An, J.; Sun, F. Research on the progress of on-orbit processing of satellite remote sensing images. Space Electron. Technol. 2023, 20, 1–8.
Zhang, S.; Li, S.; Wei, G.; Zhang, X.; Gao, J. Refined multi-scale feature-oriented object detection of remote sensing images. Natl. Remote Sens. Bull. 2022, 26, 2616–2628.
Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 23–28 June 2014; pp. 580–587.
Girshick, R. Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015; pp. 1440–1448.
Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 1137–1149.
Khanam, R.; Hussain, M. What is YOLOv5: A deep look into the internal features of the popular object detector. arXiv 2024, arXiv:2407.20892 (preprint).
Li, C.; Li, L.; Jiang, H.; Weng, K.; Geng, Y.; Li, L.; et al. YOLOv6: A single-stage object detection framework for industrial applications. arXiv 2022, arXiv:2209.02976 (preprint).
Wang, C.-Y.; Bochkovskiy, A.; Liao, H.-Y.M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 17–24 June 2023; pp. 7464–7475.
Zhou, C.; Zhu, Y.; Zhang, J.; Ding, Z.; Jiang, W.; Zhang, K. The tea buds detection and yield estimation method based on optimized YOLOv8. Sci. Hortic. 2024, 338, 113730.
Liu, H.; Chai, H.; Sun, Q.; Yun, X.; Li, X. Research status and application progress of spiking neural networks. Strateg. Study CAE 2023, 25, 61–79.
Luo, X.; Yao, M.; Chou, Y.; Xu, B.; Li, G. Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection. arXiv 2025, arXiv:2407.20708 (preprint).
Wang, A.; Chen, H.; Liu, L.; Chen, K.; Lin, Z.; Han, J.; et al. YOLOv10: Real-time end-to-end object detection. arXiv 2024, arXiv:2405.14458 (preprint).
Ouyang, D.; He, S.; Zhang, G.; Luo, M.; Guo, H.; Zhan, J.; et al. Efficient multi-scale attention module with cross-spatial learning. In Proceedings of the ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 4–10 June 2023; pp. 1–5.
Long, Y.; Gong, Y.; Xiao, Z.; Liu, Q. Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 2017, 55, 2486–2498.
Xiao, Z.; Liu, Q.; Tang, G.; Zhai, X. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images. Int. J. Remote Sens. 2015, 36, 618–644.
Panda, P.; Aketi, S.A.; Roy, K. Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization. Front. Neurosci. 2020, 14, 653.
Horowitz, M. 1.1 Computing's energy problem (and what we can do about it). In Proceedings of the 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, CA, USA, 9–13 February 2014; pp. 10–14.
Mou, D. A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes. Appl. Artif. Intell. Res. 2026, 2(1).
Li, H.; Zhao, X. Research on Auxiliary Diagnosis of Thoracic Fractures Based on Bidirectional Feature Pyramid Network. Int. J. Adv. AI Appl. 2025, 1, 23–32.
