KBN-YOLOv5: Improved YOLOv5 for Detecting Bird’s Nest in High-Voltage Tower

Cai, Sijing; Ye, Guoqi; Li, Jianxing; Kuang, Shusen; Liu, Zhenyu; Song, Jiang

doi:10.1109/cac57257.2022.10055242

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Moreover, Qiu et al [8] used a lightweight MobileNet [9] convolutional neural network as the feature extraction network for YOLOv4 [10], solving the problems of excessive model parameters and slow detection speed. Han et al [11] proposed an enhanced YOLOv5 model [12] that integrates the ECA-Net attention mechanism [13] and incorporates a bidirectional feature pyramid network in the feature fusion layer, effectively improving the accuracy of insulator defect detection. However, the model is parameter-intensive, potentially leading to computational overhead.…”

Section: Introductionmentioning

confidence: 99%

Research on Improved YOLOv8 Algorithm for Insulator Defect Detection

Zhang,

Li,

Cui

et al. 2023

Preprint

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With the rapid advancement of artificial intelligence technologies, drone aerial photography has gradually become the mainstream method for defect detection of transmission line insulators. To address the issues of slow recognition speed and low accuracy in existing detection methods, this paper proposes an insulator defect detection algorithm based on an improved YOLOv8s model. Initially, a Multi-scale Large Kernel Attention (MLKA) module is introduced to enhance the model's focus on features of different scales as well as low-level feature maps. Additionally, by employing lightweight GSConv convolution and constructing the GSC_C2f module, the computational process is simplified and memory burden is reduced, thereby effectively improving the performance of insulator defect detection. Finally, an improved loss function using SIoU is adopted to optimize the model's detection performance and enhance its feature extraction capability for insulator defects. Experimental results demonstrate that the improved model exhibits excellent performance in drone aerial photography for insulator defect detection, achieving an mAP of 99.22% and an FPS of 55.73 frames per second. Compared to the original YOLOv8s and YOLOv5s, the improved model's mAP increased by 2.18% and 2.91%, respectively, and the model size is only 30.18MB, meeting the requirements for real-time operation and accuracy.

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