Share Email Print

Proceedings Paper

Automatic solar panel recognition and defect detection using infrared imaging
Author(s): Xiang Gao; Eric Munson; Glen P. Abousleman; Jennie Si
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to “count” the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.

Paper Details

Date Published: 22 May 2015
PDF: 9 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 94760O (22 May 2015); doi: 10.1117/12.2179792
Show Author Affiliations
Xiang Gao, Arizona State Univ. (United States)
Eric Munson, Arizona State Univ. (United States)
Glen P. Abousleman, General Dynamics Mission Systems (United States)
Jennie Si, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?