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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.29126 |
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| _version_ | 1866914433385627648 |
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| author | Zhu, Yuwen Qi, Feiyang Xiang, Zhengzhe |
| author_facet | Zhu, Yuwen Qi, Feiyang Xiang, Zhengzhe |
| contents | To address the challenges of simultaneously satisfying detection accuracy, edge real-time performance, low-power operation, and end-to-end business linkage in parking scenarios, this paper proposes an intelligent parking barrier system based on deep learning and multi-sensor fusion. The system adopts a three-layer collaborative architecture comprising an edge sensing node layer, a cloud business service layer, and a front-end management application layer. On the edge side, a Raspberry Pi 5 integrates a camera, infrared ranging sensor, MPU6050 attitude sensor, and LoRa module for parking-state sensing and local decision-making. At the algorithmic level, YOLOv3-tiny is structurally pruned for single-class detection, compressing model weights to approximately 33 MB. At the decision level, an asymmetric infrared-vision-inertial fusion state machine is designed, employing an "infrared trigger - visual confirmation - inertial fallback" mechanism to enhance robustness under nighttime, occlusion, and impact disturbances. Experimental results show that after over 5000 training iterations, mAP@0.5 reaches 96.5%-98.2%. On Raspberry Pi 5, single-frame inference latency at 416x416 resolution is 600-850 ms, meeting polling requirements of 5 s (idle) and 10 s (occupied). Average power consumption decreases from 4.02 W to 1.02 W, achieving approximately 74% energy savings. Joint debugging tests further validate the solution's advantages in detection accuracy, response timeliness, energy efficiency, and engineering deployability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29126 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Sensor Fusion Parking Barrier System with Lightweight Vision on Edge Zhu, Yuwen Qi, Feiyang Xiang, Zhengzhe Networking and Internet Architecture To address the challenges of simultaneously satisfying detection accuracy, edge real-time performance, low-power operation, and end-to-end business linkage in parking scenarios, this paper proposes an intelligent parking barrier system based on deep learning and multi-sensor fusion. The system adopts a three-layer collaborative architecture comprising an edge sensing node layer, a cloud business service layer, and a front-end management application layer. On the edge side, a Raspberry Pi 5 integrates a camera, infrared ranging sensor, MPU6050 attitude sensor, and LoRa module for parking-state sensing and local decision-making. At the algorithmic level, YOLOv3-tiny is structurally pruned for single-class detection, compressing model weights to approximately 33 MB. At the decision level, an asymmetric infrared-vision-inertial fusion state machine is designed, employing an "infrared trigger - visual confirmation - inertial fallback" mechanism to enhance robustness under nighttime, occlusion, and impact disturbances. Experimental results show that after over 5000 training iterations, mAP@0.5 reaches 96.5%-98.2%. On Raspberry Pi 5, single-frame inference latency at 416x416 resolution is 600-850 ms, meeting polling requirements of 5 s (idle) and 10 s (occupied). Average power consumption decreases from 4.02 W to 1.02 W, achieving approximately 74% energy savings. Joint debugging tests further validate the solution's advantages in detection accuracy, response timeliness, energy efficiency, and engineering deployability. |
| title | A Multi-Sensor Fusion Parking Barrier System with Lightweight Vision on Edge |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2603.29126 |