GDCP-YOLO: Enhancing Steel Surface Defect Detection Using Lightweight Machine Learning Approach

Abstract

Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors.

Improved YOLOv5 Network for Aviation Plug Defect Detection

“…One-stage Faster R-CNN [34] High accuracy High computational complexity, detectors SSD [35] poor real-time YOLOv5 [24] High efficiency, Two-stage YOLOv7 [41] high accuracy, Slightly weak at detectors YOLOv8 [40] strong generalization detecting small targets YOLOv9 [42] In the early stages, to extend the lifespan of aviation plugs and enhance system reliability, a comprehensive approach includes regular preventive maintenance and the use of advanced materials coupled with real-time monitoring. Predictive maintenance, optimized through data analytics, reduces costs and focuses resources effectively.…”

“…Gao et al [39] adapted YOLOv5, enhancing the model with deconvolution computations and K-means clustering to address challenges in the effectiveness and generality of complex label text detection, thereby improving detection accuracy. Ning et al [40] proposed an improved YOLOv8 model that integrates the DCNV2 module and channel attention in C2f to combine adaptive receptive fields. Along with the efficient Faster Block, this enhancement strengthens the model architecture, simplifies feature extraction, reduces redundant information processing, and improves the accuracy and speed of multi-class steel defect detection.…”