An Anti-vibration Hammer Detection Algorithm Based on Mobilenet V3 and YOLO V3

Chen, Jianxin; Zhang, Xin; Yu, Shujiang; Yu, Qi

doi:10.1145/3480001.3480010

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Due to the demand for fast detection speed and ease of application at the front end, one-stage networks are investigated for damper defect detection. Chen et al 22 introduced a lightweight algorithm for damper detection using MobileNetv3 and YOLOv3. They performed feature extraction using MobileNetv3 and trained YOLOv3 with the extracted features.…”

Section: Cnn-based Defect Detection Methodsmentioning

confidence: 99%

Damper defect detection for transmission line based on cognitive preprocessing and feature fusion

Wu,

Chen,

Zheng

2023

J. Electron. Imag.

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.Defect detection based on deep learning has been applied in many fields and has made great progress in recent years. However, the problems of high false detection rate in complex scenes and weak multiscale detection capability have not been solved well. To solve the above problems, a damper defect detection network (DDDNet) is proposed, which includes a cognitive preprocessing network (CogniPrepNet) and a heterogeneous feature pyramid network (HeteroFPN). CogniPrepNet enhances targets and mitigates the impact of complex backgrounds through the ghost-shuffle module. HeteroFPN obtains feature maps with a wider range of scales through a many-to-one mapping relationship to achieve high-quality multiscale feature fusion. Moreover, a fast proportional intersection over union is designed to improve the sensitivity of geometric factors during the bounding box regression. The experimental results on damper-DET dataset show that the DDDNet can achieve 98.25% mAP and 34 FPS detection speed in complex background and multiscale target scene.

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Section: Cnn-based Defect Detection Methodsmentioning

confidence: 99%

Damper defect detection for transmission line based on cognitive preprocessing and feature fusion

Wu,

Chen,

Zheng

2023

J. Electron. Imag.

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“…In order to deploy network models on embedded devices, some lightweight network models are used to detect electrical component in real-time. Chen et al [26] proposed a novel model for anti-vibration hammer detection based on MobileNetV3 and YOLOv3, which obtained good detection results (accuracy of 92.6%, and running speed of 14.7 frames per second). Shi et al [27] proposed a lightweight YOLOv5 for defects detection in UAV images, which reduced the floating point operations (FLOPs) by about 50%.…”

Section: Introductionmentioning

confidence: 99%

Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet

Liu,

Lin

et al. 2024

Preprint

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As an indispensable part of transmission lines, metal fittings play an important role in the safe and stable operation of power system. Automatic detection of metal fittings in the aerial images of power inspection is an urgent problem to be solved. In this study, a deep learning method is proposed to perform metal fittings detection in UAV-based images. Firstly, metal fittings data set used for targets detection is created, including Damper, XC Clamp, and NZ Clamp. Secondly, a new deep learning model is proposed for metal fittings detection. To further improve the accuracy of metal fittings identification in aerial images, ResNet50, attention mechanism, BiFPN and Focal loss are adopted to improve RetinaNet model. Finally, the proposed model and comparative models are trained and tested on metal fittings data set. Experimental results show that the proposed model has a good effect on metal fittings detection in aerial images from power inspection. More importantly, the mAP of the proposed model can reach 93.22%, which is 7%, 9.5%, 16%, and 0.4% higher than that of original RetinaNet, Faster R-CNN, SSD and YOLOv7, respectively. And the AP values of Damper, XC Clamp and NZ Clamp detection can reach 99%, 96%, and 85% by the proposed model. In addition, compared with the mainstream target detection models, the proposed model can obtain better performance in the detection of metal fittings with complex backgrounds and small targets.

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“…With the continuous expansion of artificial intelligence applications, algorithms and models for photovoltaic fault detection are emerging in endlessly. Among them, some scholars have proposed to use models based on CNN [1] to detect the fault defects of photovoltaic panels, such as Mobilenet lightweight network [2], RepVGG reparameterized network [3], YOLO series of target detection algorithms [4], etc. These methods inherit the features of CNN's reducing the parameter quantity through weight sharing and multi-layer structure, and can scale the model according to different levels of detection requirements to detect different levels of features.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic defect detection method for marine pasture based on deep learning

Guo¹,

Qiao²,

Xu³

et al. 2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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As a large-scale marine fishery facility, marine ranch plays an important role. At the same time, in the current context of energy conservation and environmental protection, solar energy has the characteristics of clean, environmental protection, high efficiency and easy access, so it undertakes the important task of energy consumption control and energy conservation and environmental protection. However, under the influence of human and natural factors, solar panels inevitably have cracks, shielding and other defects. If it cannot be found and maintained in time, it will seriously affect the power supply efficiency and energy consumption control. Therefore, it is extremely critical to accurately detect the defects of the photovoltaic system in the marine pasture. Aiming at the problems of high false detection rate, unbalanced detection speed and accuracy in the original photovoltaic defect detection method, this paper proposes an improved method based on YOLOv5s. This method embeds CA (Coord Attention) attention mechanism in the backbone network, and uses BiFPN (Bidirectional Feature Pyramid Network) to replace the original PANet to improve the feature fusion ability. The final experimental results show that the precision of the proposed method is 1.2% higher than that of the original algorithm, and the amount of parameters and computation are reduced by 25.3% and 16.5% respectively, and the reasoning speed is the same. Therefore, this method achieves the balance between speed and accuracy, and the reduction of model size also provides the possibility for its further deployment.

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An Anti-vibration Hammer Detection Algorithm Based on Mobilenet V3 and YOLO V3

Cited by 3 publications

References 2 publications

Damper defect detection for transmission line based on cognitive preprocessing and feature fusion

Damper defect detection for transmission line based on cognitive preprocessing and feature fusion

Research on Identification of Metal Fittings in Transmission Lines Based on Improved RetinaNet

Photovoltaic defect detection method for marine pasture based on deep learning

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