YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection

• URL: http://arxiv.org/abs/2410.11727v1
• Date: Tue, 15 Oct 2024 16:00:01 GMT
• Title: YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection
• Authors: Olalekan Akindele, Joshua Atolagbe,
• Abstract summary: Existing detection methods for insulator defect identification from unmanned aerial vehicles struggle with complex background scenes and small objects. This paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second.
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• Abstract: Existing detection methods for insulator defect identification from unmanned aerial vehicles (UAV) struggle with complex background scenes and small objects, leading to suboptimal accuracy and a high number of false positives detection. Using the concept of local attention modeling, this paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. The Efficient Local Attention (ELA) blocks were added into the neck part of the one-stage YOLOv8 architecture to shift the model's attention from background features towards features of insulators with defects. The SCYLLA Intersection-Over-Union (SIoU) criterion function was used to reduce detection loss, accelerate model convergence, and increase the model's sensitivity towards small insulator defects, yielding higher true positive outcomes. Due to a limited dataset, data augmentation techniques were utilized to increase the diversity of the dataset. In addition, we leveraged the transfer learning strategy to improve the model's performance. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second, outperforming the baseline model. This further demonstrates the effectiveness of attention-based convolutional neural networks (CNN) in object detection tasks.

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