YOLOX++ for Transmission Line Abnormal Target Detection

Bi, Zhongqin; Jing, Lina; Sun, Chao; Shan, Meijing

doi:10.1109/access.2023.3268106

Cited by 10 publications

(1 citation statement)

References 39 publications

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“…However, due to the complexity of the transmission line background and the blurring of the morphological features of small targets in the image, it is difficult for traditional algorithms to recognize them by image shape and edge profile features. Deep learning-based object detection algorithms with strong feature extraction capability and good robustness can identify and localize targets more accurately [5].…”

Section: Introductionmentioning

confidence: 99%

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

Wu,

Chen,

et al. 2024

Electronics

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Wire clamps and vibration-proof hammers are key components of high-voltage transmission lines. The wire clips and vibration-proof hammers detected in Unmanned Aerial Vehicle (UAV) power inspections suffer from small size, scarce edge information, and low recognition accuracy. To address these problems, this paper proposes a small object detection (SOD) model based on the YOLOv8n, called SOD-YOLO. Firstly, an extra small target detection layer was added to YOLOv8, which significantly improves the small target detection accuracy. In addition, in order to enhance the detection speed of the model, the RepVGG/RepConv ShuffleNet (RCS) and a OneShot Aggregation of the RCS (RCSOSA) module were introduced to replace the C2f module in the model backbone and neck shallow networks. Finally, to address the excessive focus on low-quality sample bounding boxes during model training, we introduced Wise-CIoU loss instead of CIoU loss, which improved the detection accuracy of the model. The experimental results indicate that SOD-YOLO achieved a mean average precision of 90.1%, surpassing the YOLOv8n baseline model by 7.5% while maintaining a model parameter count of 3.4 M; the inference speed reached 88.7 frames/s, which meets the requirement of real-time recognition.

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Section: Introductionmentioning

confidence: 99%

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

Wu,

Chen,

et al. 2024

Electronics

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Lightweight transmission line defect identification method based on OFN network and distillation method

Pei,

Zhang,

Zhu

et al. 2024

IET Image Processing

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Edge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application.

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Insulator detection based on FA‐YOLO network with improved feature extraction ability

Jing,

Huang,

Gao

et al. 2024

IET Image Processing

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Unmanned aerial vehicle insulator detection that aims to recognize defective insulators from transmission lines has made significant progress in recent years. However, it still faces challenges, such as the complex background of aerial images and the small memory of unmanned aerial vehicles. This paper proposes a refined insulator detection algorithm that integrates the attention mechanism in YOLOv8 to improve the feature extraction ability. Specifically, this paper introduces a fast vision transformers structure in the you only look once (YOLO) v8 backbone section to enhance feature extraction by capturing local and global features. Additionally, the global attention mechanism is incorporated in the neck for additional feature extraction by merging comprehensive spatial and channel information into the output. Furthermore, we amalgamate depth‐wise convolution, graph convolution, and residual operation in the global attention mechanism module. This design can mitigate the issues of gradient vanishing or exploding and meanwhile enhance the distinction between spatial attention and channel attention. The proposed model is then applied to a public dataset and a set of real images from a specific power station, and the detection results show that it outperforms many competitors in terms of accuracy, efficiency, and memory size.

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YOLOX++ for Transmission Line Abnormal Target Detection

Cited by 10 publications

References 39 publications

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

Lightweight transmission line defect identification method based on OFN network and distillation method

Insulator detection based on FA‐YOLO network with improved feature extraction ability

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