Using Machine Learning to Identify Wildfire Risks in Electrical Pole Imagery

Business Need

Wildfires pose a devastating threat, but for Computer Science and IT engineers, they present a unique opportunity. Imagine using your skills to analyze images from pole-mounted cameras, not just for technical prowess, but to detect sparks, leaning trees, or damaged equipment – early signs of potential wildfires. This is the power of Computer Vision. Your AI models can trigger faster response times, potentially saving lives and property. Data-driven insights can even predict high-risk areas, optimizing resource allocation for prevention.

Let’s understand the business need more closely:

• Every year billions of dollars are lost in property damages by wildfires caused by sparks in  electric poles posted in the wild .
• Utility companies face significant challenges in manually inspecting and maintaining numerous poles, especially those in remote areas.
• Logistical difficulties of manual inspection and maintenance adds on to the cost of maintenance of these electric poles

Computer Vision Approach

1. Computer Vision Training:
   Utilize advanced computer vision techniques to train models in recognizing various faults in electric poles.
2. Deep Learning Model:
   Develop a Deep Learning-based image recognition model, adept at pinpointing fault components in electric poles.
3. Lightweight Model Design:
   Ensure the model’s compatibility with mobile devices and drones, focusing on lightweight and efficient operation.
4. Real-Time Detection:
   Enable real-time fault detection, offering immediate insights during inspections.
5. Cost Reduction and Efficiency:
   Dramatically cut down on manual inspection costs and time, enhancing overall efficiency and safety.

Please join the live classes to get the details. Starting on 15th of April’24.