Power line Maintenance - Implementing YOLO in NVIDIA DeepStream

Electricity plays a vital role in our daily lives. We expect continuous and uninterrupted power, but we often overlook the behind-the-scenes work that goes into keeping the lights on and the grid running. One of the most critical aspects of this work is the maintenance required for the physical generation, transmission, and distribution of energy infrastructure. This maintenance is not only needed to reduce the likelihood of outages and network failures, but also reduces the likelihood of grid-initiated wildfires such as we have seen raging across the West Coast in the past few years.

With the rapid advancements in computer vision, real-time object detection has become crucial for various applications, including surveillance and traffic control. Among the many algorithms, YOLO (You Only Look Once) stands out for its speed and efficiency. Pairing YOLO with NVIDIA DeepStream provides a robust solution for real-time video analytics. This article delves into the complexities of running YOLO on NVIDIA DeepStream, covering integration flow, optimization techniques, and practical use cases.

Introduction to YOLO and NVIDIA DeepStream

Before delving into implementation details, it’s essential to understand the components involved. YOLO is the leading real-time object detection system, making predictions in a single network pass, making it exceptionally fast for real-time applications. On the other hand, NVIDIA DeepStream is a highly optimized video analysis platform that enables scalable, high-performance, real-time AI-based video analytics at an affordable cost.

Prerequisites

Hardware: Compatible NVIDIA dGPU or Jetson.

Software: NVIDIA DeepStream SDK, YOLO model files (weights and configuration), and a development environment with the necessary dependencies.

Understanding the Workflow

The general workflow involves streaming video input via DeepStream, which then passes frames to the YOLO detector running on the GPU. The detector identifies, classifies, and processes the frames, displaying results or undergoing further processing.

Step-by-Step Integration

Preparation of the YOLO Model

• Export a YOLO model to the .onnx format for conversion by DeepStream using TensorRT, optimizing the model to leverage NVIDIA GPU architecture for faster inference times.

Configuring DeepStream

• Modify the DeepStream configuration to include the YOLO model, providing model file paths, input dimensions, and other model-specific parameters in a .config file used by DeepStream for loading and running the model.

Stream Handling

• Implement code to process input streams, which can be live video feeds or stored video files. DeepStream’s flexible architecture supports various input sources, facilitating working with video data from multiple origins.

Object Localization and Motion Analysis

• With the integrated model, DeepStream applies YOLO for object detection in video streams. Depending on the configuration, the detection process may require fine-tuning, such as setting detection thresholds.

Output Processing

• The final step involves handling the output from object detection, which may?
• include delineating objects with bounding boxes, item counting, movement tracking, or issuing alerts based on specific criteria.

Industrial application of Power Line Images with Computer Vision

Object Detection is usually used for automation of businesses worldwide. It is well-known mostly for use cases such as self-driving cars (Tesla), face detection (iPhone), and image auto-tagging (Facebook). Application of Object Detection is increasing in many industries.

This approach can also be used for the inspection of power lines to automate the analytical portion of power line visual inspections. This is done by processing the images captured from electrical facilities. Our algorithms are trained leveraging historical datasets with labeled faults. With our highly accurate and continuously learning algorithms, we can create real value through the use of AI to identify faults and failures within the critical energy infrastructure. These labels in the power sector include objects such as rust, broken infrastructure components, and vegetation surrounding the line - just to name a few. An example of such predictions is shown in the image below.

That being said, this is only the beginning of possibilities in leveraging AI in the power sector. Not only can this solution drastically reduce costs and time at scale, but our algorithms continue to improve in accuracy over time becoming more efficient, accurate, and highly personalized to each utility dataset. Utilities can use these insights to track asset health over time, enabling them to better understand the highest risk areas, rate of failures in components, and more accurately conduct maintenance. With AI, utility engineers and inspectors can make decisions faster and utilize inspection data to come to more informed decisions.

In conclusion, applications of machine learning are growing fast as it facilitates the tasks and makes them more affordable for small businesses. In particular, object detection - which is the most popular application currently in computer vision, has successful use-cases, and will help the automated machines have a better understanding of their visual targets/environment.