Work

PAL-YOLOv8: A Lightweight Algorithm for Insulator Defect Detection

Year: 2024

Type: article

Abstract: To address the challenges of high model complexity and low accuracy in detecting small targets in insulator defect detection using UAV aerial imagery, we propose a lightweight algorithm, PAL-YOLOv8. Firstly, the baseline model, YOLOv8n, is enhanced by incorporating the PKI Block from PKINet to improve the C2f module, effectively reducing the model complexity and enhancing feature extraction capabilities. Secondly, Adown from YOLOv9 is employed in the backbone and neck for downsampling, which retains more feature information while reducing the feature map size, thus improving the detection accuracy. Additionally, Focaler-SIoU is used as the bounding-box regression loss function to improve model performance by focusing on different regression samples. Finally, pruning is applied to the improved model to further reduce its size. The experimental results show that PAL-YOLOv8 achieves an mAP50 of 95.0%, which represents increases of 5.5% and 2.6% over YOLOv8n and YOLOv9t, respectively. Furthermore, GFLOPs is only 3.9, the model size is just 2.7 MB, and the parameter count is only 1.24 × 106.

Source: Electronics

DOI: 10.3390/electronics13173500

Authors: Du Zhang, Kerang Cao, Kai Han, Changsu Kim, Hoekyung Jung

Institutions: Pai Chai University, Shenyang University of Chemical Technology, Woosong University

Language: English

Topic: Power Line Inspection Robots

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: Ministry of Education