Wang Yin, Zhao Jingyong, Xie Gang, Zhao Zhicheng, Hu Xiao
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引用次数: 0
Abstract
In recent years, solar photovoltaic (PV) energy, as a clean energy source, has received widespread attention and experienced rapid growth worldwide. However, the rapid growth of PV power deployment also brings important challenges to the maintenance of PV panels, and in order to solve this problem, this paper proposes an innovative algorithm based on PA-YOLO. First, we propose to use PA-YOLO’s asymptotic feature pyramid network (AFPN) instead of YOLOv7’s backbone network to support direct interactions of nonadjacent layers and avoid large semantic gaps between nonadjacent layers. For the occlusion problem of dense targets in the dataset, we introduce a repulsive loss function, which successfully reduces the occurrence of false detection situations. Finally, we propose a customized convolutional block equipped with an EMA mechanism to enhance the perceptual and expressive capabilities of the model. Experimental results on the dataset show that our proposed model achieves excellent performance with an average accuracy (mAP) of 94.5%, which is 6.8% higher than YOLOv7. In addition, our algorithm also succeeds in drastically reducing the model size from 71.3 MB to 48.4 MB, which well demonstrates the effectiveness of the model.
期刊介绍:
International Journal of Photoenergy is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of photoenergy. The journal consolidates research activities in photochemistry and solar energy utilization into a single and unique forum for discussing and sharing knowledge.
The journal covers the following topics and applications:
- Photocatalysis
- Photostability and Toxicity of Drugs and UV-Photoprotection
- Solar Energy
- Artificial Light Harvesting Systems
- Photomedicine
- Photo Nanosystems
- Nano Tools for Solar Energy and Photochemistry
- Solar Chemistry
- Photochromism
- Organic Light-Emitting Diodes
- PV Systems
- Nano Structured Solar Cells