How I Built a Drone-Based Crack Detection Pipeline on AWS
Roberto Belotti details the development of a drone-based pipeline for detecting structural defects using AWS. The project focuses on processing drone footage to identify issues like cracks and corrosion, emphasizing the engineering challenges beyond just model training. The article outlines the architecture choices made for efficient data handling and reporting to engineers.
- ▪The pipeline processes drone footage to detect surface defects and deliver actionable reports.
- ▪Three options for inference were explored: full edge, full cloud, and a hybrid approach.
- ▪The chosen method involves lightweight triage during flight, followed by full-resolution processing in the cloud.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 104343) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Roberto Belotti Posted on May 19 How I Built a Drone-Based Crack Detection Pipeline on AWS #aws #python #computervision #devops I needed to build a pipeline that takes drone footage of infrastructure (bridges, facades, roads), detects surface defects like cracks and corrosion, and delivers actionable reports to engineers who don't care about ML. Sounds straightforward. It wasn't.
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