AI System for Solar Panel Fault Detection with Live DemoAI System for Solar Panel Fault Detection with Live Demo
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I built an AI system that detects solar panel faults from a single image โ€” and deployed it with a live demo. Here's what it does: ๐Ÿฒ ๐—ณ๐—ฎ๐˜‚๐—น๐˜ ๐—ฐ๐—น๐—ฎ๐˜€๐˜€๐—ฒ๐˜€: Bird-drop, Dusty, Snow-Covered, Electrical Damage, Physical Damage, Clean ย โ€ข ResNet101 fine-tuned on solar panel imagery ย โ€ข 96.3% training accuracy with strong per-class F1 ย โ€ข REST API deployed on Hugging Face Spaces (Docker + Flask) ย โ€ข React frontend with real-time confidence scores ๐—ช๐—ต๐˜† ๐˜๐—ต๐—ถ๐˜€ ๐—บ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐˜€: According to Raptor Maps' 2025 Global Solar Report, equipment-driven underperformance has increased 214% since 2019 โ€” resulting in $10 billion in lost energy value in 2024 alone. The average solar site loses 5.77% of its power output annually, costing ~$5,720 per MWdc. The use cases that excite me most: ย โ€ข Drone inspection over utility-scale farms ย โ€ข Ground robotics for close-up panel analysis ย โ€ข Direct integration into SCADA systems via REST API ๐—•๐˜‚๐—ถ๐—น๐˜ ๐˜„๐—ถ๐˜๐—ต: PyTorch ยท ResNet101 ยท Flask ยท Docker ยท HuggingFace Spaces ยท React + Vite
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