DBCW-YOLO: A Modified YOLOv5 for the Detection of Steel Surface Defects
Source abstract
Abstract
In steel production, defect detection is crucial for preventing safety risks, and improving the accuracy of steel defect detection in industrial environments remains challenging due to the variable types of defects, cluttered backgrounds, low contrast, and noise interference. Therefore, this paper introduces a steel surface defect detection model, DBCW-YOLO, based on YOLOv5. Firstly, a new feature fusion strategy is proposed to optimize the feature map fusion pair model using the BiFPN method to fuse information at multiple scales, and CARAFE up-sampling is introduced to expand the sensory field of the network and make more effective use of the surrounding information. Secondly, the WIoU uses a dynamic non-monotonic focusing mechanism introduced in the loss function part to optimize the loss function and solve the problem of accuracy degradation due to sample inhomogeneity. This approach improves the learning ability of small target steel defects and accelerates network convergence. Finally, we use the dynamic heads in the network prediction phase. This improves the scale-aware, spatial-aware, and task-aware performance of the algorithm. Experimental results on the NEU-DET dataset show that the average detection accuracy is 81.1, which is about (YOLOv5) 6% higher than the original model and satisfies real-time detection. Therefore, DBCW-YOLO has good overall performance in the steel surface defect detection task.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Non-Destructive Testing Techniques · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Funding
intelligent monitoring and decision-making system for train operation status at the station · design of a sample bank based on intelligent detection of railway trains
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