Year: 2025
Type: article
Abstract: Aiming at the problem of high false detection rate of PCB defect detection,an improved YOLOv8-C2f_DBB-SPFF_LSKA (YOLOv8-CDSL) is used to detect defects of the PCB.The diverse branch block (DBB) is used to improve the faster version of CSP bottle-neck with two convolutions (C2f) module in the YOLOv8 backbone network.The large separable kernel attention (LSKA) mechanism is also added to the SPPF module of the YOLOv8 network.The C2F_DBB module can significantly improve the model's ability to identify small-scale PCB defects, and greatly enhance the model's performance in comprehensive feature extraction.Thus significantly improving the overall accuracy of the model.The SPPF_LSKA module can reduce the computing power consumption of model training.Consequently, it significantly enhances the detection capability of the improved YOLOv8-CDSL network.The effectiveness of the improved scheme was verified by ablation experiments.At the same time, it is verified by comparative experiments that the improved YOLOv8-CDSL network has the highest detection accuracy of 99.13% for six common surface defects of PCB.
DOI: 10.70517/ijhsa471102
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Funder: Hubei Polytechnic University
OpenAlex ID: W4414448553
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