Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment
The normal operation of power equipment plays a critical role in the power system, making anomaly detection for power equipment highly significant. This paper proposes an improved YOLOv7x-based anomaly detection algorithm for power equipment. First, the ACmix convolutional mixed attention mechanism module is introduced to effectively suppress background noise and irrelevant features, thereby enhancing the network's feature extraction capability. Second, the Biformer attention mechanism is added to the network to strengthen the focus on key features, improving the network's ability to flexibly recognize feature images. Finally, to more comprehensively evaluate the relationship between predicted and ground truth bounding boxes, the original loss function is replaced with the MPDIoU function, addressing the issue of mismatched predicted bounding boxes. The improved algorithm enhances detection accuracy, achieving a mAP@0.5/% of 93.5% for all target categories, a precision of 97.1%, and a recall of 97%.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionDefect DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Comparative Analysis of Object Detection Algorithms for Surface Defect Detection
This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representi…
Real-Time Object DetectionIDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection Algorithm
In order to achieve real-time detection of insulator defects in transmission lines, this paper proposes an improved insulator defect detection algorithm based on the YOLOv5s model, named IDD-YOLOv5. Firstly, to enhance t…
Defect DetectionInsulator Defect DetectionObject DetectionSmall Object DetectionLiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection
Insulator defect detection is of great significance to ensure the normal operation of power transmission and distribution networks. In response to the problems of low speed, low accuracy, and difficulty in deploying to e…
Defect DetectionInsulator Defect DetectionModel Compressionobject-detection+1Surface Defects Detection of Transparent Plastic Bottles Based on Improved Yolov5
Given the high demand for transparent plastic bottles in our daily lives, coupled with their appearance being a crucial quality indicator, it is imperative to pay close attention to detecting surface defects on these bot…
object-detectionObject DetectionObject RecognitionDefect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11
This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifyi…
Data AugmentationDefect DetectionGenerative Adversarial Network