Scholarlink

<p> This paper presents a deep learning-based approach for detecting surface defects on automotive engine injector valve seats. Current inspection processes typically rely on manual visual assessment or traditional machine vision; however, both methods often fail to meet the necessary requirements for speed and accuracy. While conventional machine vision offers high throughput, it remains susceptible to environmental factors—such as variations in lighting, camera positioning, and background interference—which limits its robustness and feature extraction capabilities. To address these limitations, this paper proposes an optimized deep learning algorithm designed to improve defect detection performance in manufacturing environments. We implement and compare improved Faster R-CNN and YOLO algorithms for surface defect detection. Although the enhanced Faster R-CNN achieves high coverage, it still suffers from false negatives. Conversely, the YOLO-tiny and YOLO-CBAM-tiny models provide better detection accuracy, especially for scratch and black area defects. Specifically, the inclusion of the Convolutional Block Attention Module (CBAM) allows the YOLO-CBAM-tiny network to significantly outperform the standard YOLO-tiny model in identifying black defects. By addressing these aspects, this study aligns with Sustainable Development Goal, which prioritizes the advancement of resilient and sustainable manufacturing infrastructure.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Surface flaw detection of small automotive component based on improved faster R-CNN and YOLO algorithms

  • 期刊论文

  • 发表日期：

    2026年5月1日

查看全文
  • Wei Li,
  • Fahri Heltha,
  • Aulia Rahman,
  • Mahmud Iwan Solihin

摘要

This paper presents a deep learning-based approach for detecting surface defects on automotive engine injector valve seats. Current inspection processes typically rely on manual visual assessment or traditional machine vision; however, both methods often fail to meet the necessary requirements for speed and accuracy. While conventional machine vision offers high throughput, it remains susceptible to environmental factors—such as variations in lighting, camera positioning, and background interference—which limits its robustness and feature extraction capabilities. To address these limitations, this paper proposes an optimized deep learning algorithm designed to improve defect detection performance in manufacturing environments. We implement and compare improved Faster R-CNN and YOLO algorithms for surface defect detection. Although the enhanced Faster R-CNN achieves high coverage, it still suffers from false negatives. Conversely, the YOLO-tiny and YOLO-CBAM-tiny models provide better detection accuracy, especially for scratch and black area defects. Specifically, the inclusion of the Convolutional Block Attention Module (CBAM) allows the YOLO-CBAM-tiny network to significantly outperform the standard YOLO-tiny model in identifying black defects. By addressing these aspects, this study aligns with Sustainable Development Goal, which prioritizes the advancement of resilient and sustainable manufacturing infrastructure.