Version 1
: Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (16:48:51 CEST)
How to cite:
Zou, Y.; Zhang, Z.; Zhou, C.; Tan, Y. Lightweight Chip Pad Real-Time Alignment Detection Method and Application Based on Improved YOLOv5s. Preprints2024, 2024081058. https://doi.org/10.20944/preprints202408.1058.v1
Zou, Y.; Zhang, Z.; Zhou, C.; Tan, Y. Lightweight Chip Pad Real-Time Alignment Detection Method and Application Based on Improved YOLOv5s. Preprints 2024, 2024081058. https://doi.org/10.20944/preprints202408.1058.v1
Zou, Y.; Zhang, Z.; Zhou, C.; Tan, Y. Lightweight Chip Pad Real-Time Alignment Detection Method and Application Based on Improved YOLOv5s. Preprints2024, 2024081058. https://doi.org/10.20944/preprints202408.1058.v1
APA Style
Zou, Y., Zhang, Z., Zhou, C., & Tan, Y. (2024). Lightweight Chip Pad Real-Time Alignment Detection Method and Application Based on Improved YOLOv5s. Preprints. https://doi.org/10.20944/preprints202408.1058.v1
Chicago/Turabian Style
Zou, Y., Chiyang Zhou and Yufei Tan. 2024 "Lightweight Chip Pad Real-Time Alignment Detection Method and Application Based on Improved YOLOv5s" Preprints. https://doi.org/10.20944/preprints202408.1058.v1
Abstract
Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.
Keywords
deep learning; chip detection; small target detection; lightweighting
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
