Paper
23 August 2024 Substation foreign object intrusion detection algorithm based on YOLOv5
Xiaopeng Ruan, Li Lu
Author Affiliations +
Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 1325034 (2024) https://doi.org/10.1117/12.3038542
Event: Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia
Abstract
To address the issue of detecting foreign objects invading substations, which are difficult to detect and easily obscured, this paper suggests an object recognition method utilizing YOLOv5s. Firstly, the C3 module in the backbone network is improved to enhance its feature extraction capability. Secondly, the DCNv2 convolution and SimAM attention mechanism are used to improve SPPFCSPC and serve as the model's spatial pyramid pooling module, allowing the model to better utilize the feature information between the lower and upper layers, thereby improving model performance. Lastly, F-CIOU is used as the model's loss function to balance the issue of significant differences in the number of easy and difficult samples, further enhancing the model's detection accuracy. The experimental findings indicate that the enhanced model’s mean average precision (mAP) has risen by 2.1% over the baseline model, achieving a mAP of 91.9%, which suffices for real-world detection requirements.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xiaopeng Ruan and Li Lu "Substation foreign object intrusion detection algorithm based on YOLOv5", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 1325034 (23 August 2024); https://doi.org/10.1117/12.3038542
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KEYWORDS
Object detection

Convolution

Data modeling

Performance modeling

Feature extraction

Statistical modeling

Detection and tracking algorithms

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