Improved YOLOv7 model for insulator defect detection

2025-02-11

Electronic Research Archive

AI Summary

To address the low detection accuracy and poor robustness in identifying small insulator defects—characterized by multi-material composition, multi-color appearance, and complex backgrounds—in power transmission lines, this paper proposes an enhanced YOLOv7 model. Specifically, the SPPCSPC module is replaced with a Receptive Field Block (RFB) to enlarge the receptive field; Coordinate Attention (CA) is integrated to strengthen discriminative feature representation; and the WIoU loss function is adopted to improve convergence on hard examples. Experimental results demonstrate that, while maintaining real-time inference capability, the proposed method achieves 1.6% improvements in both mAP₀.₅ and mAP₀.₅:₀.₉₅, along with 1.3% higher precision and 1.0% higher recall. Moreover, it reduces model parameters by 3.2 million, computational cost by 2.5 GFLOPS, and single-image inference latency by 2.81 ms—achieving a favorable trade-off among high accuracy, strong robustness, and lightweight design.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (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. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds.

Problem

• Enhances insulator defect detection accuracy
• Improves multi-type insulator defect identification
• Reduces model parameters and computational cost

Innovation

• Enhanced YOLOv7 model
• RFB module feature extraction
• CA mechanism accuracy boost