When starting an object detection project, the initial focus is often on building the most accurate model possible. However, highly accurate models are usually not deployable in production scenarios due to the trade-off between accuracy and computational demands. These models tend to be resource-intensive and can be impractical and costly to deploy. Deploying models on edge devices, such as the NVIDIA Jetson, can be highly cost-effective in the long run. However, edge devices have limited computational resources, making it challenging to run complex models efficiently. Another set of constraints in edge devices is power consumed and heat dissipated, which also restricts the processing throughput. In this article, we will discuss the key considerations for implementing object detection on edge devices.

Choice of Object Detection Framework:

When deploying an object detection framework on edge devices, selecting the appropriate model architecture is crucial to balance computational efficiency, latency, and accuracy. While two-stage detectors such as Faster R-CNN provide high accuracy by first generating region proposals and then refining detections, their computational demands and complexity make them less suited for edge environments, which typically have limited processing power and memory. Single-stage detectors, like YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), are often more suitable for edge deployments due to their streamlined, one-pass approach, which offers a good trade-off between speed and accuracy, making them more efficient for real-time inference. Additionally, transformer-based object detection models, while powerful, are often computationally intensive, making single-stage detectors a preferred choice for edge devices. The ability of single-stage models to operate with lower latency and resource consumption makes them ideal for scenarios requiring quick, on-device decision-making, such as in autonomous drones, security cameras, or IoT applications.

In conclusion, selecting an object detection network for deployment on an edge device involves considering several factors, including compute power, memory resources, and performance requirements. For optimal results, it’s essential to balance these parameters effectively. So, the next time you need to choose an object detector for your application, make sure you pick the right one!