Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD.

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Tác giả: Chao Chen, Zhuo Chen, Guangzhou Lei, Hao Li, Yawen Wang, Lingling Wu

Ngôn ngữ: eng

Ký hiệu phân loại: 616.0277 Diseases

Thông tin xuất bản: Switzerland : Sensors (Basel, Switzerland) , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 79988

Given the high computational complexity and poor real-time performance of current photovoltaic cell surface defect detection methods, this study proposes a lightweight model, YOLOv8-FSD, based on YOLOv8. By introducing the FasterNet network to replace the original backbone network, computational complexity and memory access are reduced. A thin neck structure designed based on hybrid convolution technology is adopted to reduce model parameters and computational load further. A lightweight dynamic feature upsampling operator improves the feature map quality. Additionally, the regularized Gaussian distribution distance loss function is used to enhance the detection ability for small target defects. Experimental results show that the YOLOv8-FSD lightweight algorithm improves detection accuracy while significantly reducing the number of parameters and computational requirements compared to the original algorithm. This improvement provides an efficient, accurate, and lightweight solution for PV cell defect detection.
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