When deploying object detection models on edge devices, several trade-offs must be carefully considered:
Model Complexity vs. Inference Speed: More complex models, such as YOLO8_m, typically offer higher accuracy but require more computational resources, leading to longer inference times. Practitioners must balance the need for accuracy with the real-time processing requirements of applications, especially in scenarios like autonomous vehicles where latency is critical.
Hardware Capabilities vs. Model Size: Edge devices have limited processing power, memory, and energy resources. High-accuracy models may exceed the capabilities of lower-end devices like Raspberry Pi 3, necessitating the use of more powerful hardware (e.g., Jetson Orin Nano). This can increase costs and complexity in deployment.
Energy Consumption vs. Performance: High-performance models often consume more energy, which can be a significant constraint for battery-powered devices. Researchers must evaluate the energy efficiency of models and consider using accelerators to mitigate energy costs while maintaining performance.
Deployment Environment Constraints: The specific deployment environment (e.g., outdoor vs. indoor, varying lighting conditions) can affect model performance. Models may need to be fine-tuned or adapted to handle these conditions, which can increase development time and complexity.
Scalability vs. Customization: While a one-size-fits-all model may simplify deployment, it may not perform optimally across diverse applications. Customizing models for specific tasks can improve performance but may complicate the deployment process and require additional resources for training and maintenance.
Real-time Processing vs. Accuracy: In applications requiring real-time processing, such as surveillance or autonomous driving, there may be a need to sacrifice some accuracy for faster inference times. This trade-off must be carefully managed to ensure safety and effectiveness in critical applications.