ST-YOLO: A defect detection method for photovoltaic

First, it introduces the C2f-SCconv convolution module, which is based on SCconv convolution. This module reduces the computational burden of model

A lightweight and efficient model for photovoltaic panel defect

Within this research, we introduce a streamlined yet effective model founded on the “You Only Look Once” algorithm to detect photovoltaic panel defects in intricate settings.

An effective approach to improving photovoltaic defect

A custom dataset was constructed by combining a public PV panel defect database with field-collected images, further expanded through data

Fault Detection and Classification for Photovoltaic

To tackle these issues, a new machine-learning model will be presented. This model can accurately identify and categorize defects by

Enhanced photovoltaic panel defect detection via

To tackle this challenge, we propose an Adaptive Complementary Fusion (ACF) module designed to intelligently integrate spatial and channel

Photovoltaic panel composition formula detection

To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction.

LEM-Detector: An Efficient Detector for Photovoltaic Panel Defect

To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture.

YOLO-PPM: a lightweight object detector with multi-frequency

Furthermore, intricate environmental interferences, heterogeneous panel appearances, and heavy occlusions exacerbate these challenges. To this end, we propose YOLO-PPM, a lightweight

A photovoltaic panel defect detection framework

This paper presents a lightweight object detection algorithm based on an improved YOLOv11n, specifically designed for photovoltaic panel defect

A Photovoltaic Panel Defect Detection Method Based on the Improved

Aiming at the current PV panel defect detection methods with insufficient accuracy, few defect categories, and the problem that defect targets cannot be localized, this paper proposes a PV panel