Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Wu, Jun; Fan, Ping; Sun, Yang; Gui, Weifeng

doi:10.32604/cmes.2022.020919

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…These algorithms perform better than traditional machine vision methods in terms of detection accuracy and speed [28]. In 2020, Wu et al [29] put forward an improved deep learning object detection algorithm RetinaNet to detect bubbles on the surface of pharmaceutical empty bottles, in response to issues such as weak robustness and weak resistance to noise interference. Through validation on the dataset, the mean average precision (mAP) increased by nearly 2.4% compared to the original algorithm.…”

Section: Deep Learning Based Detection Methodsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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Bubble detection has important applications in industries such as tightness testing, fluid measurement, healthcare, and chemical engineering. Due to the fact that bubbles belong to small targets with characteristics such as reflection and shadows, they have fewer available features and are highly susceptible to environmental interference, which makes it difficult for detection models to accurately locate and recognize the bubbles. Aiming at these problems, an improved YOLOv8n for bubble detection is proposed. Firstly, the deformable convolution network (DCN) is used in the Backbone module to take place of the original C2f module, which enables the model to have stronger feature extraction and adaptive generalization capabilities for small targets with different deformations. Then the global attention mechanism (GAM) is introduced into Neck network to locally enhance the channels or regions of interest, which is beneficial for capturing the important features of targets, especially small targets like bubbles. Finally, the loss function is improved so that boundary box regression and target detection become more accurate. Experiments are conducted on the bubble dataset, and the results indicate the mean average precision (mAP) of the improved algorithm reaches 97.4%, increasing by 2.2% compared to the original YOLOv8n.This shows the proposed method has better comprehensive performance on bubble detection.

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Section: Deep Learning Based Detection Methodsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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“…The single-stage target detection network, RetinaNet 24 , 25 , has been improved to better suit the detection of electrical equipment, which often has a large aspect ratio, a tilt angle, and is densely arranged. The horizontal rectangular frame of the original RetinaNet has been altered to a rotating rectangular frame to accommodate the prediction of the tilt angle of the electrical equipment.…”

Section: Refined Detection Of Complex Electrical Equipmentmentioning

confidence: 99%

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

Tang,

Jian

2024

Sci Rep

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This paper realizes infrared image denoising, recognition, and semantic segmentation for complex electrical equipment and proposes a thermal fault diagnosis method that incorporates temperature differences. We introduce a deformable convolution module into the Denoising Convolutional Neural Network (DeDn-CNN) and propose an image denoising algorithm based on this improved network. By replacing Gaussian wrap-around filtering with anisotropic diffusion filtering, we suggest an image enhancement algorithm that employs Weighted Guided Filtering (WGF) with an enhanced Single-Scale Retinex (Ani-SSR) technique to prevent strong edge halos. Furthermore, we propose a refined detection algorithm for electrical equipment that builds upon an improved RetinaNet. This algorithm incorporates a rotating rectangular frame and an attention module, addressing the challenge of precise detection in scenarios where electrical equipment is densely arranged or tilted. We also introduce a thermal fault diagnosis approach that combines temperature differences with DeeplabV3 + semantic segmentation. The improved RetinaNet's recognition results are fed into the DeeplabV3 + model to further segment structures prone to thermal faults. The accuracy of component recognition in this paper achieved 87.23%, 86.54%, and 90.91%, with respective false alarm rates of 7.50%, 8.20%, and 7.89%. We propose a comprehensive method spanning from preprocessing through target recognition to thermal fault diagnosis for infrared images of complex electrical equipment, providing practical insights and robust solutions for future automation of electrical equipment inspections.

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“…The high density and overlap of small objects in the shadow of photovoltaic panels pose great difficulties for real-time detection. Jun W et al [6] improved the RetinaNet algorithm model. Firstly, they proposed the Ghost CSP DenseNet feature extraction network to reduce network size and improve network detection speed.…”

Section: Introductionmentioning

confidence: 99%

Research on Object Detection and Recognition Method for UAV Aerial Images Based on Improved YOLOv5

Zhang

Shao

et al. 2023

Drones

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In this paper, an object detection and recognition method based on improved YOLOv5 is proposed for application on unmanned aerial vehicle (UAV) aerial images. Firstly, we improved the traditional Gabor function to obtain Gabor convolutional kernels with better edge enhancement properties. We used eight Gabor convolutional kernels to enhance the object edges from eight directions, and the enhanced image has obvious edge features, thus providing the best object area for subsequent deep feature extraction work. Secondly, we added a coordinate attention (CA) mechanism to the backbone of YOLOv5. The plug-and-play lightweight CA mechanism considers information of both the spatial location and channel of features and can accurately capture the long-range dependencies of positions. CA is like the eyes of YOLOv5, making it easier for the network to find the region of interest (ROI). Once again, we replaced the Path Aggregation Network (PANet) with a Bidirectional Feature Pyramid Network (BiFPN) at the neck of YOLOv5. BiFPN performs weighting operations on different input feature layers, which helps to balance the contribution of each layer. In addition, BiFPN adds horizontally connected feature branches across nodes on a bidirectional feature fusion structure to fuse more in-depth feature information. Finally, we trained the overall improved YOLOv5 model on our integrated dataset LSDUVD and compared it with other models on multiple datasets. The results show that our method has the best convergence effect and mAP value, which demonstrates that our method has unique advantages in processing detection tasks of UAV aerial images.

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Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Cited by 6 publications

References 46 publications

Research on Bubble Detection Based on Improved YOLOv8n

Research on Bubble Detection Based on Improved YOLOv8n

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

Research on Object Detection and Recognition Method for UAV Aerial Images Based on Improved YOLOv5

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