2000 character limit reached
EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
Published 20 Mar 2024 in cs.CV | (2403.14027v3)
Abstract: Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment. We propose a difficulty-aware edge-cloud collaborative sensing system that splits the task into object localization and fine-grained classification. Objects are classified either at the edge or within the cloud, based on their estimated difficulty. The framework comprises a low-power device-tailored front-end model for object localization, classification, and difficulty estimation, along with a transformer-graph convolutional network-based back-end model for fine-grained classification. Our system demonstrates superior performance ([email protected] +4.3%}) on widely used marine object detection datasets, significantly reducing both data transmission volume (by 95.43%) and energy consumption (by 72.7%}) at the system level. We validate the proposed system across various embedded system platforms and in real-world scenarios involving drone deployment.
- Hanning Chen et al. 2023. HyperGRAF: Hyperdimensional Graph-Based Reasoning Acceleration on FPGA. In 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 34–41.
- Po-Yung Chou et al. 2023. Fine-grained Visual Classification with High-temperature Refinement and Background Suppression. arXiv preprint arXiv:2303.06442 (2023).
- Ross Girshick. 2015. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision. 1440–1448.
- Matthieu Guillaumin et al. 2014. Imagenet auto-annotation with segmentation propagation. International Journal of Computer Vision 110 (2014), 328–348.
- Kai Han et al. 2020. Ghostnet: More features from cheap operations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1580–1589.
- Xiangteng He et al. 2017. Weakly supervised learning of part selection model with spatial constraints for fine-grained image classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.
- Geoffrey Hinton et al. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015).
- Qibin Hou et al. 2021. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 13713–13722.
- Jie Hu et al. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132–7141.
- Wenjun Huang et al. 2024. A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission. arXiv preprint arXiv:2402.02043 (2024).
- Bogdan Iancu et al. 2021. Aboships—an inshore and offshore maritime vessel detection dataset with precise annotations. Remote Sensing 13, 5 (2021), 988.
- Ziyu Jia et al. 2023. Exploiting Interactivity and Heterogeneity for Sleep Stage Classification Via Heterogeneous Graph Neural Network. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5.
- Kinjal A Joshi et al. 2012. A survey on moving object detection and tracking in video surveillance system. International Journal of Soft Computing and Engineering 2, 3 (2012), 44–48.
- Jun-Hwa Kim et al. 2022. Object detection and classification based on YOLO-V5 with improved maritime dataset. Journal of Marine Science and Engineering 10, 3 (2022), 377.
- Alex Krizhevsky et al. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
- Hyunsei Lee et al. 2023. Comprehensive Integration of Hyperdimensional Computing with Deep Learning towards Neuro-Symbolic AI. In 2023 60th ACM/IEEE Design Automation Conference (DAC). IEEE, 1–6.
- Sung-Jun Lee et al. 2018. Image-based ship detection and classification for unmanned surface vehicle using real-time object detection neural networks. In ISOPE International Ocean and Polar Engineering Conference. ISOPE, ISOPE–I.
- Di Lin et al. 2019. Zigzagnet: Fusing top-down and bottom-up context for object segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7490–7499.
- Ryan Wen Liu et al. 2021a. An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system. Ocean Engineering 235 (2021), 109435.
- Ze Liu et al. 2021b. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision. 10012–10022.
- Yu Mao et al. 2022. TRACE: A Fast Transformer-based General-Purpose Lossless Compressor. In Proceedings of the ACM Web Conference 2022. 1829–1838.
- Yang Ni et al. 2022. Online performance and power prediction for edge TPU via comprehensive characterization. In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 612–615.
- Joseph Redmon et al. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788.
- Shaoqing Ren et al. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015).
- Zhenfeng Shao et al. 2018. Seaships: A large-scale precisely annotated dataset for ship detection. IEEE transactions on multimedia 20, 10 (2018), 2593–2604.
- J Sharafaldeen et al. 2022. Marine Object Detection Based on Top-View Scenes Using Deep Learning on Edge Devices. In 2022 International Conference on Smart Systems and Power Management (IC2SPM). IEEE, 35–40.
- Bharat Singh et al. 2018. R-fcn-3000 at 30fps: Decoupling detection and classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1081–1090.
- Hanyuan Wang et al. 2018. Multi-scale fusion with context-aware network for object detection. In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2486–2491.
- Xiu-Shen Wei et al. 2021. Fine-grained image analysis with deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence 44, 12 (2021), 8927–8948.
- Qingze Yu et al. 2021. Object detection-tracking algorithm for unmanned surface vehicles based on a radar-photoelectric system. IEEE Access 9 (2021), 57529–57541.
- Sanggeon Yun et al. 2024. HyperSense: Accelerating Hyper-Dimensional Computing for Intelligent Sensor Data Processing. arXiv preprint arXiv:2401.10267 (2024).
- Qirui Zhang et al. 2023. An Ultra-Low-Power H. 264/AVC Intra-Frame Image Compression Accelerator for Intelligent Event-Driven IoT Imaging Systems. IEEE Solid-State Circuits Letters (2023).
- Ruolan Zhang et al. 2021. Survey on deep learning-based marine object detection. Journal of Advanced Transportation 2021 (2021), 1–18.
- Yu Zhang et al. 2016. Weakly supervised fine-grained categorization with part-based image representation. IEEE Transactions on Image Processing 25, 4 (2016), 1713–1725.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.