The defect of silicon photovoltaic (PV) modules excited by photoluminescence (PL) technology at high light level (HLL) will be easily drowned in the ambient light; therefore, the detection equipment cannot sense the defect information directly. To solve this problem, a defect detection method that effectively resists the interference of ambient light in daytime is proposed in this paper. This method uses modulated light as the excitation for PV modules, and employs the near-infrared (NIR) camera to capture a group of image sequences satisfying the modulation characteristics. To restore the defect information of PV modules from image sequences, an algorithm based on time domain error is proposed. Finally, hardware acceleration of algorithm by field programmable gate array is implemented to solve the problem of time-consuming tasks for the personal computer. The experimental results show that this method can effectively restore the defect pattern with HLL extending from 0.1 lx to 11,170 ± 5 lx. Therefore, this work can provide an effective detection strategy for PL detection of PV modules at HLL.
A natural-color mapping for single-band night-time image method based on FPGA can transmit the color of the reference image to single-band night-time image, which is consistent with human visual habits and can help observers identify the target. This paper introduces the processing of the natural-color mapping algorithm based on FPGA. Firstly, the image can be transformed based on histogram equalization, and the intensity features and standard deviation features of reference image are stored in SRAM. Then, the real-time digital images’ intensity features and standard deviation features are calculated by FPGA. At last, FPGA completes the color mapping through matching pixels between images using the features in luminance channel.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.