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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Related Experiment Video

Updated: Jun 12, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Near-infrared defect detection method for photovoltaic panels based on the FBCT-YOLOv8 algorithm.

Zhongxing Zhang¹, Shaotong Pei², Chenlong Hu³

¹Yanzhao Electric Power Laboratory, Department of Electrical Engineering, North China Electric Power University (Baoding), Baoding 071032, Baoding, 071003, Hebei Province, China. 2935726429@qq.com.

Scientific Reports

|June 10, 2026

Summary

This summary is machine-generated.

This study introduces FBCT-YOLOv8 for enhanced near-infrared defect detection in photovoltaic panels. The method improves accuracy for small defects and diverse morphologies, meeting real-time performance needs.

Keywords:

Defect detection Near-infrared ODConv Photovoltaic panels YOLOv8

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Last Updated: Jun 12, 2026

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Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass (ADG) Fresnel Lens for Concentrating Photovoltaics

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Published on: October 27, 2017

Area of Science:

Materials Science
Electrical Engineering
Computer Vision

Background:

Near-infrared defect detection in photovoltaic panels faces challenges with varying defect scales, morphologies, and small target identification.
Existing methods struggle to adapt to the complex visual characteristics of defects, impacting overall detection efficiency and accuracy.

Purpose of the Study:

To propose an advanced defect detection method for photovoltaic panels using near-infrared imaging.
To enhance the accuracy and efficiency of detecting diverse and small-scale defects in solar panels.

Main Methods:

The FBCT-YOLOv8 algorithm, built upon YOLOv8, incorporates omni-dimensional dynamic convolution (ODConv) for adaptive feature extraction.
A lightweight cross-scale feature fusion module (CCFM) is utilized for improved integration of multi-scale features, enhancing adaptability to scale variations.
Dynamic head (DyHead) and InnerWIoU loss function are employed to refine detection performance and loss calculation.

Main Results:

The FBCT-YOLOv8 algorithm achieved a GFLOPs reduction to 7.0 and an mAP@50 of 0.91.
The method demonstrates real-time performance suitable for photovoltaic panel defect detection.
Ablation and comparative experiments confirmed the proposed method's effectiveness and superiority over existing approaches.

Conclusions:

The proposed FBCT-YOLOv8 algorithm effectively addresses the challenges in near-infrared defect detection for photovoltaic panels.
The integration of ODConv, CCFM, DyHead, and InnerWIoU significantly improves detection accuracy, especially for small and varied defects.
The method achieves a balance between high accuracy and real-time processing, making it suitable for practical applications.