Improved YOLOv7 model for insulator defect detection
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
Insulators are crucial insulation components and structural supports in power
grids, playing a vital role in the transmission lines. Due to temperature
fluctuations, internal stress, or damage from hail, insulators are prone to
injury. Automatic detection of damaged insulators faces challenges such as
diverse types, small defect targets, and complex backgrounds and shapes. Most
research for detecting insulator defects has focused on a single defect type or
a specific material. However, the insulators in the grid's transmission lines
have different colors and materials. Various insulator defects coexist, and the
existing methods have difficulty meeting the practical application
requirements. Current methods suffer from low detection accuracy and mAP0.5
cannot meet application requirements. This paper proposes an improved YOLOv7
model for multi-type insulator defect detection. First, our model replaces the
SPPCSPC module with the RFB module to enhance the network's feature extraction
capability. Second, a CA mechanism is introduced into the head part to enhance
the network's feature representation ability and to improve detection accuracy.
Third, a WIoU loss function is employed to address the low-quality samples
hindering model generalization during training, thereby improving the model's
overall performance. The experimental results indicate that the proposed model
exhibits enhancements across various performance metrics. Specifically, there
is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in
mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall.
Moreover, the model achieves parameter reduction by 3.2 million, leading to a
decrease of 2.5 GFLOPS in computational cost. Notably, there is also an
improvement of 2.81 milliseconds in single-image detection speed.
