Comprehensive Benchmarking of Deep Learning Object Detection Models on Edge Computing Devices

Alapfogalmak

This study comprehensively evaluates the performance of popular deep learning object detection models, including YOLOv8, EfficientDet Lite, and SSD, across various edge computing devices such as Raspberry Pi 3, 4, 5, Pi with TPU accelerators, and NVIDIA Jetson Orin Nano. The evaluation focuses on key metrics including inference time, energy consumption, and accuracy (mean Average Precision).

Kivonat

The researchers developed object detection applications using Flask-API and deployed the models on the edge devices using different frameworks like PyTorch, TensorFlow Lite, and TensorRT. They employed the FiftyOne tool to evaluate the accuracy of the models on the COCO dataset and used the Locust tool for automated performance measurement, including energy consumption and inference time.

The key findings are:

• SSD_v1 exhibits the fastest inference times, while YOLO8_m has the highest accuracy but also the highest energy consumption.
• The addition of TPU accelerators to the Raspberry Pi devices significantly improves the performance of the SSD and YOLO8 models in terms of inference time and energy efficiency.
• The Jetson Orin Nano emerges as the most energy-efficient and fastest device overall, particularly for the YOLO8 models, despite having the highest idle energy consumption.
• The results highlight the need to balance accuracy, speed, and energy efficiency when deploying deep learning models on edge devices, providing valuable guidance for practitioners and researchers.

Statisztikák

The Raspberry Pi 3 has a base energy consumption of 270 mWh, while the Raspberry Pi 4 and 5 have 199 mWh and 217 mWh, respectively.
The energy consumption per request (excluding base energy) for the SSD_v1 model ranges from 0.01 mWh on Jetson Orin Nano to 0.31 mWh on Raspberry Pi 3.
The inference time for the SSD_v1 model ranges from 12 ms on Pi 4 with TPU to 427 ms on Raspberry Pi 3.
The mean Average Precision (mAP) for the YOLO8_m model ranges from 32 on Pi 5 with TPU to 44 on Raspberry Pi 4 and Jetson Orin Nano.

Idézetek

"SSD_v1 exhibits the lowest inference time among all evaluated models."
"Jetson Orin Nano stands out as the fastest and most energy-efficient option for request handling, despite having the highest idle energy consumption."
"The results highlight the need to balance accuracy, speed, and energy efficiency when deploying deep learning models on edge devices."

Főbb Kivonatok

Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices

by Daghash K. A... : arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16808.pdf

Mélyebb kérdések

How can the performance of object detection models be further improved on edge devices, especially for high-accuracy models like YOLO8_m?

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.

What are the potential trade-offs between model complexity, hardware capabilities, and real-world deployment constraints that researchers and practitioners should consider?

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.

How can the insights from this study be applied to optimize the deployment of object detection systems in diverse edge computing applications, such as autonomous vehicles, surveillance, or smart city infrastructure?

The insights from this study can be instrumental in optimizing the deployment of object detection systems across various edge computing applications:

Model Selection Based on Application Needs: Practitioners can use the performance metrics (inference time, energy consumption, and accuracy) reported in the study to select the most suitable object detection model for their specific application. For instance, SSD models may be preferred for applications requiring faster inference times, while YOLO8 models may be chosen for scenarios where accuracy is paramount.

Hardware Compatibility: The findings highlight the importance of matching models with appropriate hardware. For applications like autonomous vehicles that require real-time processing, deploying models on high-performance devices like Jetson Orin Nano can ensure that the system meets the necessary speed and accuracy requirements.

Energy Efficiency Considerations: The study emphasizes the trade-offs between energy consumption and model performance. In battery-operated devices, such as drones or mobile surveillance units, selecting energy-efficient models and utilizing accelerators can extend operational time while maintaining performance.

Scalability and Adaptability: The insights can guide the development of scalable object detection systems that can adapt to different environments and requirements. For example, using a modular approach where different models can be deployed based on the context (e.g., urban vs. rural settings) can enhance system flexibility.

Real-time Data Processing: The deployment of edge computing solutions allows for real-time data processing, which is crucial for applications like smart city infrastructure and surveillance. The study's findings can help in designing systems that process data locally, reducing latency and reliance on cloud services.

Continuous Monitoring and Improvement: The evaluation metrics provided in the study can serve as benchmarks for ongoing performance monitoring. Practitioners can use these metrics to iteratively improve their models and deployment strategies, ensuring that the systems remain effective as conditions and requirements evolve.

By applying these insights, researchers and practitioners can enhance the effectiveness and efficiency of object detection systems in various edge computing applications, ultimately leading to improved safety, performance, and user experience.