Research on lightweight vehicle pedestrian detection model and Android deployment
Object detection models usually have a large number of parameters,making them inapplicable on mobile devices.Against this backdrop,we propose a lightweight vehicle and pedestrian detection model,YOLOv8-TI(Traffic Information).A novel lightweight parameter-sharing SPG Detect detection head is designed to reduce the model's parameters and computational load.The Global Balanced Channel Path Aggregation Network(GBC-PAN)structure is proposed to balance the number of network channels and achieve bidirectional feature fusion from top-down and bottom-up directions through weighted connections across scales.Meanwhile,a dynamic non-monotonic focusing mechanism,represented by the Wise Loss function,is introduced to enhance the accuracy of predicted bounding boxes.Our experimental results reveal the YOLOv8-TI model maintains a high accuracy rate while reducing the parameters,flops,and model volume by 52.1％,58.0％and 54％respectively compared with those of YOLOv8n.A comparative analysis with other lightweight object detection algorithms verifies the effectiveness and superiority of our method.YOLOv8-TI is put on Android mobile devices and tested on Honor 20 fps and Honor 80GT,achieving frame rates of 24 and 31 FPS respectively,meeting real-time requirements.It is set to accomplish traffic information detection tasks when applied on autonomous driving vehicles.
deep learningcar and pedestrian detectionsharing Parameterlight weight
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