To enhance the performance of high-accuracy object detection models like YOLO8_m on edge devices, several strategies can be employed:
Model Optimization Techniques: Techniques such as model pruning, quantization, and knowledge distillation can significantly reduce the model size and computational requirements without substantially sacrificing accuracy. For instance, pruning can remove redundant weights, while quantization can convert floating-point weights to lower precision formats, making the model more efficient for edge deployment.
Use of Hardware Accelerators: Leveraging specialized hardware accelerators such as TPUs (Tensor Processing Units) or GPUs can drastically improve inference speed and energy efficiency. The study indicates that integrating TPUs with Raspberry Pi models leads to reduced energy consumption and improved inference times, which can be particularly beneficial for YOLO8_m.
Dynamic Model Selection: Implementing a dynamic model selection mechanism based on the current computational load and available resources can optimize performance. For example, switching between different model sizes (e.g., YOLO8_n, YOLO8_s) based on the context can help maintain a balance between accuracy and resource usage.
Edge Computing Frameworks: Utilizing optimized frameworks like TensorRT for NVIDIA Jetson devices or TensorFlow Lite for Raspberry Pi can enhance the execution efficiency of deep learning models. These frameworks are designed to maximize the performance of models on specific hardware, thus improving inference times.
Data Augmentation and Transfer Learning: Employing data augmentation techniques during training can help improve the robustness of the model, allowing it to generalize better to unseen data. Additionally, transfer learning from pre-trained models can accelerate training and improve accuracy, especially in resource-constrained environments.
Algorithmic Improvements: Continuous research into novel architectures and algorithms can lead to more efficient models. For instance, exploring lightweight versions of YOLO or integrating attention mechanisms can enhance detection capabilities while maintaining low computational overhead.