Defect detection method of printed circuit boards based on EDF-YOLOv10
Zhijuan Shen, Yonger Yao, Lin Liu, Yiqing Cao, Lijun Lu
TL;DR
This paper introduces an improved YOLOv10 algorithm, EDF-YOLOv10, for efficient and accurate detection of defects in printed circuit boards.
Contribution
The novel EDF-YOLOv10 integrates ECA, DSConv, and Focaler-CIoU loss to enhance small object detection and convergence speed in PCB inspection.
Findings
EDF-YOLOv10 achieves 90.6% [email protected] and 48.4% [email protected]:0.95 on the experimental dataset.
The method improves performance by 3.0 and 1.6 percentage points over the baseline model.
A real-time detection system using EDF-YOLOv10 is developed for industrial PCB inspection.
Abstract
To address the challenges of inadequate feature representation for small objects and slow model convergence in printed circuit board (PCB) defect detection, this paper proposes an improved YOLOv10 algorithm and develops a real-time detection system with a co-optimized hardware and software architecture. The efficient channel attention (ECA) mechanism is used to enhance the ability of the model to extract key channel features; the dynamic snake convolution (DSConv) in the backbone strengthens the model’s capacity to recognize the geometric structures of small targets through deformable kernels and multi-directional feature fusion; the Focaler-CIoU loss emphasizes samples with low intersection over union (IoU) values to boost hard sample learning and improve convergence efficiency. To simulate real-world industrial environments, multiple data augmentation strategies are utilized to expand…
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Taxonomy
TopicsAdvanced Neural Network Applications · Industrial Vision Systems and Defect Detection · Advanced Data and IoT Technologies