Defect Detection Algorithm Research Articles

Automotive Parts Defect Detection Based on YOLOv7

Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection algorithms have been proven to be ineffective, which often leads to missing detection or wrong identification. To address these issues, this paper introduces a defect detection algorithm based on YOLOv7. To enhance the detection of small objects and streamline the model, we incorporate the ECA attention mechanism into the network structure’s backbone. Considering the small sizes of defect targets on automotive parts and the complexity of their backgrounds, we redesign the neck portion of the model. This redesign includes the integration of the BiFPN feature fusion module to enhance feature fusion, with the aim of minimizing missed detections and false alarms. Additionally, we employ the Alpha-IoU loss function in the prediction phase to enhance the model’s accuracy, which is crucial for reducing false detection. The IoU loss function also boosts the model’s efficiency at converging. The evaluation of this model utilized the Northeastern University steel dataset and a proprietary dataset and demonstrated that the average accuracy (mAP) of the MBEA-YOLOv7 detection network was 76.2% and 94.1%, respectively. These figures represent improvements of 5.7% and 4.7% over the original YOLOv7 network. Moreover, the detection speed for individual images ranges between 1–2 ms. This enhancement in detection accuracy for small targets does not compromise detection speed, fulfilling the requirements for real-time, dynamic inspection of defects.

YOLOv8-LMG: An Improved Bearing Defect Detection Algorithm Based on YOLOv8

In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing defect detection model, YOLOv8-LMG, which is based on the YOLOv8n framework and incorporates four innovative technologies: the VanillaNet backbone network, the Lion optimizer, the CFP-EVC module, and the Shape-IoU loss function. These enhancements significantly increase detection efficiency and accuracy. YOLOv8-LMG achieves a mAP@0.5 of 86.5% and a mAP@0.5–0.95 of 57.0% on the test dataset, surpassing the original YOLOv8n model while maintaining low computational complexity. Experimental results reveal that the YOLOv8-LMG model boosts accuracy and efficiency in bearing defect detection, showcasing its significant potential and practical value in advancing industrial inspection technologies.

Research on Surface Defect Detection of Strip Steel Based on Improved YOLOv7.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Printed circuit board (PCB) defect detection is an important and indispensable part of industrial production. PCB defects, due to the small target and similarity between classes, in the actual production of the detection process are prone to omission and false detection problems. Traditional machine-learning-based detection methods are limited by the actual needs of industrial defect detection and do not show good results. Aiming at the problems related to PCB defect detection, we propose a PCB defect detection algorithm based on DSASPP-YOLOv5 and conduct related experiments on the PKU-Market-PCB dataset. DSASPP-YOLOv5 is an improved single-stage detection model, and we first used the K-means++ algorithm for the PKU-Market-PCB dataset to recluster the model so that the model is more in line with the characteristics of PCB small target defects. Second, we design the Depthwise Separable Atrous Spatial Pyramid Pooling (DSASPP) module, which effectively improves the correlation between local and global information by constructing atrous convolution branches with different dilated rates and a global average pooling branch. The experimental results show that our model achieves satisfactory results in both the mean average precision and detection speed metrics compared to existing models, proving the effectiveness of the proposed method.

PECT Composite Defect Detection Algorithm Based on DualGAN

To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

Printed Circuit Board Defect Detection Algorithm Based on yolov5s Structure Improvement

In terms of the existing PCB defect detection algorithm in complicated model structure with huge numbers of parameters slow speed, low detection accuracy and other problems, an improved PCB defect detection algorithm which is based upon yolov5s is proposed. Taking the yolov5s model as the main framework, the Omni-Dimensional Dynamic Convolution (OdConv) structure is used to replace the backbone of the original model, and the Convolutional Block Attention Module (CBAM) attention mechanism is introduced after the Conv layer of the Neck network. CBAM attention mechanism is brought in after the Conv layer of the Neck network (Neck), and the Focal-EIoU Loss function is modified by the Complete Intersection and Merger Ratio Loss (CIoU Loss) function, and different weights are assigned to high-quality samples and low-quality samples respectively, which solves sample imbalance existing in the target detection problem, and the results show that this algorithm is comparable to the original model. Experiments are conducted and the results show that this paper’s algorithm improves the mAP by 1.9%, speeds up by 47.75Fps, decrease the model size by 13.26 M, and reduces the computation volume by 12.3 G compared to yolov5s at IoU = 0.5.

PCB defect detection algorithm based on CDI-YOLO

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages.

Improved YOLOv5-based pore defect detection algorithm for wire arc additive manufacturing

The identification and detection of microstructural defects in wire arc additive manufacturing (WAAM) specimens play a significant role in characterizing and analyzing the mechanical properties of these specimens. Porosity is one of the primary forms of microstructural defects in WAAM specimens. Therefore, in this paper, a model for detecting pore defects in the cross-section of WAAM specimens based on the YOLOv5s model is proposed. The proposed model incorporates several improvements to enhance its speed and accuracy. Firstly, a lightweight backbone network is constructed to improve the detection speed by introducing the GhostConv and PConv modules. Secondly, an efficient spatial pooling pyramid structure (ESPP) is designed to enhance model processing speed and detection accuracy. Thirdly, the neck network incorporates a newly designed double path aggregation network (DPAN) to enhance the preservation of intricate details within the network. Finally, the C3 module in the YOLOv5s feature fusion network is improved, and the GC3ECA module is proposed by combining the efficient channel attention mechanism ECA and GhostConv module to enhance channel information and reduce redundant information. Tests on the self-built WAAM defect dataset and NEU-DET dataset show that the mean average precision（mAP）of the improved model is 90.4% and 76.5%, which is 1.9% and 3.8% higher than that of the YOLOv5s model, respectively. Meanwhile, the detection speed of the improved model reaches 74.21 frames per second (FPS), which is 38.7% higher than YOLOv5s.The experimental results show that the improved model has better overall performance.

Advancements in Steel Surface Defect Detection: An Enhanced YOLOv5 Algorithm with EfficientNet Integration

Steel surface defect detection is of utmost importance for ensuring product quality, cost reduction, enhanced safety, and heightened customer satisfaction. To address the limitations of traditional steel surface defect detection algorithms, which often yielded singular detection results and suffered from high miss detection rates, we proposed an enhanced Yolov5 steel surface defect detection algorithm. In this approach, this paper employed the EfficientNet network as a replacement for the Yolov5 backbone network. Subsequently, we trained and tested this modified network on a steel surface defect dataset to mitigate the challenges associated with high miss detection rates and underperforming evaluation metrics. Our experimental findings underscored the superiority of the improved algorithm, particularly when compared to Yolov5. This enhanced algorithm exhibited substantial improvements across several key performance metrics, including Precision, Recall, mAP@0.5, parameter count, and pt file size. Noteworthy achievements included a 6.39% increase in Precision for Yolov5-EfficientNetB4, a remarkable 7.75% improvement in Recall for Yolov5-EfficientNetB0, and a 5.57% boost in mAP@0.5 for Yolov5-EfficientNetB6. Additionally, the pt file size for Yolov5-EfficientNetB0 saw a substantial 39.65% reduction, although it was important to note that the inference time for the improved algorithm increased. Among the models, Yolov5-EfficientNetB6 struck the best balance in terms of performance.

A supervised approach to automatic density and foreign-object detection in particleboard paving quality control

ABSTRACT Density detection and foreign object detection are very significant to ensure the quality of particleboard production and are important links in the quality control of pavement. This study analyzes the density uniformity of the paving process by average surface density, horizontal and vertical zoned average surface density based on CT values detected by X-rays, and studies the parts and causes of density non-uniformity. A foreign object detection scheme of alarming and then detecting is proposed to detect the presence of foreign objects in paving boards by MobileNetV3. Classification and localization of defects are performed by the improved YOLOv8 model with a multi-window sliding detection method, which introduces the backbone network of MobileNetV3 to simplify the network structure, and enhances the feature extraction capability and the detection of small targets by improving the C2f of YOLOv8 and increasing the detection branches. The experimental results show that the detection precision of the foreign object alarm is 96.72%, the recall rate is 95.80%, and the mAP value is 95.65%. In the defect slice dataset, the improved YOLOv8 defect detection algorithm has a detection precision of 97.96%, a recall rate of 97.60%, and a mAP value of 89.55%.

3D attention-focused pure convolutional target detection algorithm for insulator defect detection

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%.

Defect Detection Algorithm for Battery Cell Casings Based on Dual-Coordinate Attention and Small Object Loss Feedback

To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production.

Research on fabric surface defect detection algorithm based on improved Yolo_v4

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites.

Multi-scale surface defect detection method for bottled products based on variable receptive fields and Gather–Distribute feature fusion mechanism

The inspection of visual quality represents a crucial step in the production process of bottled products. Numerous machine vision methodologies have demonstrated proficient identification of significant defects on bottle surfaces in well-controlled imaging environments. However, the actual production scenario introduces a myriad of surface defect types on bottled products, exhibiting diverse shapes, with the majority of defect instances characterized by relatively small individual areas. Faced with such diverse and small-sized defects, traditional convolutional neural networks (CNNs) encounter limitations due to the utilization of fixed-size convolutional kernels. This choice results in a constrained receptive field, hindering the capture of sufficiently effective contextual information. Additionally, pooling operations within traditional CNNs lead to a substantial reduction in feature map dimensions, causing excessive blurring or outright neglect of small-sized defects. Consequently, this detrimentally impacts the accuracy of surface defect detection and recognition. This study proposes a novel multi-scale defect detection model incorporating a variable receptive field and Gather–Distribute feature fusion mechanism to overcome limitations of traditional CNNs. Nine different scale defect images of bottled products, including worn, wrinkles, joint markings and etc, were collected and enhanced to construct a surface defect detection dataset for the actual production of bottled products. Enhancements to convolutional layers and the C2f module improve feature extraction for different defect shapes and sizes. Integration of the Gather–Distribute feature fusion module (GDFFM) reduces feature information loss and enhances shallow-layer feature utilization. An efficient detection head (E-Detect) with ”parameter sharing” is proposed to reduce computational complexity and improve detection speed without experiencing significant accuracy loss. Experimental results demonstrate that the model’s superiority over advanced defect detection algorithms across various categories. Notably, it achieves accuracies of 89.9%, 55.6%, 89.4%, and 75% for challenging defects like small worn, subtle wrinkles, inclined external cover, and misaligned labels, with improvements of 3.2%, 7%, 2%, and 11.8% over the baseline model, respectively. The model’s mean Average Precision (mAP) also increases by 2.7%.

Usage of an improved YOLOv5 for steel surface defect detection

Abstract The one-stage YOLOv5 steel surface defect detection has issues such as slow operation speed, loss of defect location and semantic information of small targets, and inadequate extraction of defect features. This study proposed a defect detection algorithm with improved YOLOv5 to solve these issues. The proposed algorithm used the slim-neck layer built by three new modules instead of the neck layer in YOLOv5s to achieve a lightweight network model. In addition, the spatial perception self-attention mechanism was introduced to enhance the feature extraction capability of the initial convolutional layer without limiting the input size. The improved Atrous Spatial Pyramid Pooling was added to expand the perceptual field and capture multiscale contextual information while preventing local information loss and enhancing the relevance of long-range information. The experimental results showed that the improved YOLOv5 algorithm has a reduced model volume, significantly higher detection accuracy and speed than the traditional algorithm, and the ability to detect steel surface defects quickly and accurately.

Steel Surface Defect Detection Algorithm Based on YOLOv8

To improve the accuracy of steel surface defect detection, an improved model of multi-directional optimization based on the YOLOv8 algorithm was proposed in this study. First, we innovate the CSP Bottleneck with the two convolutions (C2F) module in YOLOv8 by introducing deformable convolution (DCN) technology to enhance the learning and expression ability of complex texture and irregular shape defect features. Secondly, the advanced Bidirectional Feature Pyramid Network (BiFPN) structure is adopted to realize the weight distribution learning of input features of different scales in the feature fusion stage, allowing for more effective integration of multi-level feature information. Next, the BiFormer attention mechanism is embedded in the backbone network, allowing the model to adaptively allocate attention based on target features, such as flexibly and efficiently skipping non-critical areas, and focusing on identifying potentially defective parts. Finally, we adjusted the loss function from Complete-Intersection over Union (CIoU) to Wise-IoUv3 (WIoUv3) and used its dynamic non-monotony focusing property to effectively solve the problem of overfitting the low quality target bounding box. The experimental results show that the mean Average Precision (mAP) of the improved model in the task of steel surface defect detection reaches 84.8%, which depicts a significant improvement of 6.9% compared with the original YOLO8 model. The improved model can quickly and accurately locate and classify all kinds of steel surface defects in practical applications and meet the needs of steel defect detection in industrial production.

The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning

As a critical foundational component, bearings find widespread application in various mechanical equipment. In order to achieve automated defect detection in the bearing-manufacturing process, a defect detection algorithm combining magnetic particle inspection with deep learning is proposed. Dynamic thresholding and generative adversarial network (GAN) methods are employed to extract defect samples from bearing images and augment the dataset, thereby enhancing data diversity. To mitigate the impact of irrelevant displays in bearing images, a coordinated attention (CA) mechanism is introduced into the backbone network of the deep learning model to focus on key information. Additionally, an adaptive spatial feature fusion module (ASFF) is incorporated during the multiscale fusion stage to maintain consistency in features across different hierarchical levels. The weighted intersection over union (WIoU) bounding box loss function is utilized to replace the original generalized intersection over union (GIoU) in the network, directing the model’s attention towards common-quality anchor boxes to reduce the adverse effects of inconsistent annotations. The experimental results demonstrate that the improved network achieves a mean average precision (mAP) of 98.4% on the bearing dataset, representing a 4.2% improvement over the original network.

A rail fastener defect detection algorithm based on improved YOLOv5

Being a crucial component of railway tracks, monitoring the health condition of fasteners stands as a critical aspect within the realm of railroad track management, ensuring the normal passage of trains. However, traditional track fastener detection methods mainly use artificial checks, giving rise to challenges encompassing reduced efficiency, safety hazards, and poor detection accuracy. Consequently, we introduce an innovative model for the detection of track fastener defects, termed YOLOv5-CGBD. In this study, we first imbue the backbone network with the CBAM attention mechanism, which elevates the network’s emphasis on pertinent feature extraction within defective regions. Subsequently, we replace the standard convolutional blocks in the neck network with the GSConv convolutional module, achieving a delicate balance between the model’s accuracy and computational speed. Augmenting our model’s capacities for efficient feature map fusion and reorganization across diverse scales, we integrate the weighted bidirectional feature pyramid network (BiFPN). Ultimately, we manipulate a lightweight decoupled head structure, which improves both detection precision and model robustness. Concurrently, to enhance the model’s performance, a data augmentation strategy is employed. The experimental findings testify to the YOLOv5-CGBD model’s ability to conduct real-time detection, with mAP0.5 scores of 0.971 and 0.747 for mAP0.5:0.95, surpassing those of the original YOLOv5 model by 2.2% and 4.1%, respectively. Furthermore, we undertake a comparative assessment, contrasting the proposed methodology with alternative approaches. The experimental outcomes manifest that the YOLOv5-CGBD model exhibits the most exceptional comprehensive detection performance while concurrently maintaining a high processing speed.

An Algorithm for Detecting Surface Defects in Steel Strips Based on an Improved Lightweight Network

In recent years, surface defect detection methods based on deep learning have been widely applied to steel plate surface defect detection. By locating and classifying defects on the surface of steel plates, production efficiency can be improved. However, there is still a conflict between speed and accuracy in the defect detection process. To address this issue, we propose a high-precision, low-latency surface defect detection algorithm called the GhostConv-ECA-YOLOv5 Network (GEA-Net). The GEA-Net model can predict defect categories without compromising classification and detection accuracy. Experimental results show that our proposed improved model has higher performance compared to other comparative models, achieving a 75.6% mAP on the NEU-DET dataset.