Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

Real-Time Steel Surface Defect Detection with Improved Multi-Scale YOLO-v5

In the field of metallurgy, the timely and accurate detection of surface defects on metallic materials is a crucial quality control task. However, current defect detection approaches face challenges with large model parameters and low detection rates. To address these issues, this paper proposes a lightweight recognition model for surface damage on steel strips, named LSD-YOLOv5. First, we design a shallow feature enhancement module to replace the first Conv structure in the backbone network. Second, the Coordinate Attention mechanism is introduced into the MobileNetV2 bottleneck structure to maintain the lightweight nature of the model. Then, we propose a smaller bidirectional feature pyramid network (BiFPN-S) and combine it with Concat operation for efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, the Soft-DIoU-NMS algorithm is employed to enhance the recognition efficiency in scenarios where targets overlap. Compared with the original YOLOv5s, the LSD-YOLOv5 model achieves a reduction of 61.5% in model parameters and a 28.7% improvement in detection speed, while improving recognition accuracy by 2.4%. This demonstrates that the model achieves an optimal balance between detection accuracy and speed, while maintaining a lightweight structure. 

LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy.

Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network

In industrial manufacturing, metal surface defect detection often suffers from low detection accuracy, high leakage rates, and false detection rates. To address these issues, this paper proposes a novel model named DSL-YOLO for metal surface defect detection. First, we introduce the C2f_DWRB structure by integrating the DWRB module with C2f, enhancing the model’s ability to detect small and occluded targets and effectively extract sparse spatial features. Second, we design the SADown module to improve feature extraction in challenging tasks involving blurred images or very small objects. Finally, to further enhance the model’s capacity to extract multi-scale features and capture critical image information (such as edges, textures, and shapes) without significantly increasing memory usage and computational cost, we propose the LASPPF structure. Experimental results demonstrate that the improved model achieves significant performance gains on both the GC10-DET and NEU-DET datasets, with a mAP@0.5 increase of 4.2% and 2.6%, respectively. The improvements in detection accuracy highlight the model’s ability to address common challenges while maintaining efficiency and feasibility in metal surface defect detection, providing a valuable solution for industrial applications. 

Research on a Metal Surface Defect Detection Algorithm Based on DSL-YOLO

改进YOLOv5s的钢板表面缺陷检测算法

For deep learning-based object detection, we present a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios. First of all, multi-scale information is integrated into different feature dimensions to improve the distinction and robustness of features. The design of the detectors increases the variety of detection boxes to accommodate a wider range of detected objects. Secondly, the pooling method is upgraded to obtain more detailed information. At last, we add the Angle cost and assign new weights to different loss functions to accelerate the convergence and improve the accuracy of network detection. In our network, we explore four variants MSS-YOLOv5s, MSS-YOLOv5m, MSS-YOLOv5x, and MSS-YOLOv5l. Experimental results of MSS-Yolov5s show that our technique improves mAP on the PASCAL VOC2007 and PASCAL 2012 datasets by 2.4% and 2.9%, respectively. Meanwhile, it maintains a fast inference speed. At the same time, the other three models have different degrees of performance improvement in terms of balancing speed and precision in challenging detection regions.

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

To solve the problem of low accuracy and slow speed in the surface defect detection of steel, an improved YOLOv4 algorithm is proposed. Firstly, the SE attention mechanism is embedded between the trunk and neck of the original YOLOv4 model, which can improve the model's ability to capture global information. Secondly, the ICIoU function is introduced to solve the problem that the CIoU function will degenerate when the aspect ratio of the prediction box and the real box is equal. The experiments are carried out on the NEU-DET dataset, the results show that the average accuracy of the improved YOLOv4 algorithm is 78.63%, which is 6.98% higher than that of the original algorithm; the detection speed is 60fps. The model fully meets the requirements of rapid and accurate detection of the production line.

Steel Surface Defect Detection Algorithm based on Improved YOLOv4

It is well known that controller design is often unavoidable for extreme and zero stability, and that unstable poles (zero) limit the performance of the controller. In this paper, we study the limit zeros and poles of the discrete-time model where the sampling period tends to zero in the case of zero-order hold (ZOH) or generalized sampling-data hold function (GSHF). We first introduce GSHF and give its transfer function. Then we discuss the distribution of zero polarity with discrete time systems using ZOH or GSHF, and give their stability behavior. When the GSHF is used for signal reconstruction, the stability of the limit zero of the discrete-time system can be significantly improved and a discrete-time design with a stable zero in the case of GSHF is given.

Analytic study on the poles and zeros for mechanical plants

The invention provides a soft measuring method for residual capacity of a lead-acid battery based on neural network transparency. A neural network has excellent nonlinear mapping approximation capability and is widely applied to modeling in chemical process. However, a neural network modeling method belongs to a black box method, and the obtained model is lack of transparency with poor interpretation of each variable, resulting in restricting guiding on optimizing technical decisions for chemical enterprises. By combing three methods of a neural network definition diagram, a connection weightmethod and an improved randomized test, a neural network model in the complex chemical process is performed with transparency study, comprising the following steps: firstly visualizing the model by using the neural network definition diagram, then performing quantificational analysis on the contribution rate of a decision parameter with the connection weight method, and finally performing significance test on the connection weight of the model, and the comprehensive contribution and the relative contribution rate of the decision parameter by using the randomized test, thereby trimming the model. The study on validation of a hydrocyanic acid production model in complex chemical process represents that the method acquires internal information of process variable, and greatly improves the "understandable" ability of the model.

Soft measuring method for residual capacity of lead-acid battery based on neural network transparency

This paper proposes a new soft computing method based on simplified BP neural network. For the traditional fully connected BP neural network, the optimal structure 8-5-5-1 is obtained through the optimization of the hidden layer, and the output weight and the threshold are obtained. Since the input and output structure of the obtained lead-acid battery is a “black box”, a calculation method of the contribution rate of the weight and the threshold is proposed, and the “black box” is transparent according to the method. By comparing the accuracy of BP neural network before and after simplification, the accuracy has no effect, but the model is simplified, and it is easier to observe the connection strength within the model.

Soft Computing Detection Method for Remaining Capacity of Lead-Acid Battery

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xiaofeng Wang

Author Tools

Chat about Author