A Multi-Sensor Fusion Parking Barrier System with Lightweight Vision on Edge
This addresses parking management efficiency with an incremental engineering improvement for real-world deployment.
The paper tackled the challenge of creating an efficient parking barrier system by proposing a multi-sensor fusion approach with lightweight vision on edge devices, achieving 96.5%-98.2% mAP@0.5 accuracy and reducing power consumption by approximately 74%.
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.