Search Results (67)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article

Recent advances in precision manufacturing and high-end equipment technologies have imposed ever more stringent requirements on the accuracy, real-time performance, and lightweight design of online steel strip surface defect detection systems. To reconcile the persistent trade-off between detection precision and inference efficiency in complex industrial environments, this study proposes StripSurface–YOLO, a novel real-time defect detection framework built upon YOLOv8n. The core architecture integrates an Efficient Cross-Stage Local Perception module (ResGSCSP), which synergistically combines GSConv lightweight convolutions with a one-shot aggregation strategy, thereby markedly reducing both model parameters and computational complexity. To further enhance multi-scale feature representation, this study introduces an Efficient Multi-Scale Attention (EMA) mechanism at the feature-fusion stage, enabling the network to more effectively attend to critical defect regions. Moreover, conventional nearest-neighbor upsampling is replaced by DySample, which produces deeper, high-resolution feature maps enriched with semantic content, improving both inference speed and fusion quality. To heighten sensitivity to small-scale and low-contrast defects, the model adopts Focal Loss, dynamically adjusting to sample difficulty. Extensive evaluations on the NEU-DET dataset demonstrate that StripSurface–YOLO reduces FLOPs by 11.6% and parameter count by 7.4% relative to the baseline YOLOv8n, while achieving respective improvements of 1.4%, 3.1%, 4.1%, and 3.0% in precision, recall, mAP₅₀, and mAP_50:95. Under adverse conditions—including contrast variations, brightness fluctuations, and Gaussian noise—SteelSurface-YOLO outperforms the baseline model, delivering improvements of 5.0% in mAP₅₀ and 4.7% in mAP_50:95, attesting to the model’s robust interference resistance. These findings underscore the potential of StripSurface–YOLO to meet the rigorous performance demands of real-time surface defect detection in the metal forging industry. Full article

Steel surface defect detection is a crucial step in ensuring industrial production quality. However, due to significant variations in scale and irregular geometric morphology of steel surface defects, existing detection algorithms show notable deficiencies in multi-scale feature representation and cross-layer multi-scale feature fusion efficiency. To address these challenges, this paper proposes an improved real-time steel surface defect detection model, NGD-YOLO, based on YOLOv5s, which achieves fast and high-precision defect detection under relatively low hardware conditions. Firstly, a lightweight and efficient Normalization-based Attention Module (NAM) is integrated into the C3 module to construct the C3NAM, enhancing multi-scale feature representation capabilities. Secondly, an efficient Gather–Distribute (GD) mechanism is introduced into the feature fusion component to build the GD-NAM network, thereby effectively reducing information loss during cross-layer multi-scale information fusion and adding a small target detection layer to enhance the detection performance of small defects. Finally, to mitigate the parameter increase caused by the GD-NAM network, a lightweight convolution module, DCConv, that integrates Efficient Channel Attention (ECA), is proposed and combined with the C3 module to construct the lightweight C3DC module. This approach improves detection speed and accuracy while reducing model parameters. Experimental results on the public NEU-DET dataset show that the proposed NGD-YOLO model achieves a detection accuracy of 79.2%, representing a 4.6% mAP improvement over the baseline YOLOv5s network with less than a quarter increase in parameters, and reaches 108.6 FPS, meeting the real-time monitoring requirements in industrial production environments. Full article

Lightweight visual models are crucial for industrial defect detection tasks. Traditional methods and even some lightweight detectors often struggle with the trade-off between high computational demands and insufficient accuracy. To overcome these issues, this study introduces YOLO-MAD, an innovative model optimized through a multi-scale geometric structure feature extraction and fusion scheme. YOLO-MAD integrates three key modules: AKConv for robust geometric feature extraction, BiFPN to facilitate effective multi-scale feature integration, and Detect_DyHead for dynamic optimization of detection capabilities. Empirical evaluations demonstrate significant performance improvements: YOLO-MAD achieves a 5.4% mAP increase on the NEU-DET dataset and a 4.8% mAP increase on the GC10-DET dataset. Crucially, this is achieved under a moderate computational load (9.4 GFLOPs), outperforming several prominent lightweight models in detection accuracy while maintaining comparable efficiency. The model also shows enhanced recognition performance for most defect categories. This work presents a pioneering approach that balances lightweight design with high detection performance by efficiently leveraging multi-scale geometric feature extraction and fusion, offering a new paradigm for industrial defect detection. Full article

In resistive polymer humidity sensors, the quality of the resistor chips directly affects the performance. Detecting chip defects remains challenging due to the scarcity of defective samples, which limits traditional supervised-learning methods requiring abundant labeled data. While few-shot learning (FSL) shows promise for industrial defect detection, existing approaches struggle with mixed-scene conditions (e.g., daytime and night-version scenes). In this work, we propose a crossed wavelet convolution network (CWCN), including a dual-pipeline crossed wavelet convolution training framework (DPCWC) and a loss value calculation module named ProSL. Our method innovatively applies wavelet transform convolution and prototype learning to industrial defect detection, which effectively fuses feature information from multiple scenarios and improves the detection performance. Experiments across various few-shot tasks on chip datasets illustrate the better detection quality of CWCN, with an improvement in mAP ranging from 2.76% to 16.43% over other FSL methods. In addition, experiments on an open-source dataset NEU-DET further validate our proposed method. Full article

Addressing the difficulties in identifying surface defects in steel and various industrial materials, including challenges in detection, low generalization, and poor robustness, as well as the shortcomings of existing algorithms for industrial applications, this paper presents the YOLO-LSDI algorithm for steel surface defect identification. First, the model integrates the Adaptive Multi-Scale Pooling–Fast (AMSPPF) module, an adaptive multi-scale pooling approach that improves the extraction of global semantic and local edge features. Second, the Deformable Spatial Attention Module (DSAM), a hybrid attention mechanism combining deformable and spatial attention, is introduced to enhance the network’s focus on defect-relevant regions under complex industrial backgrounds. Third, Linear Deformable Convolution (LDConv) replaces standard convolution to better adapt to the irregular shapes of defects while maintaining low computational cost. Finally, the Inner-Complete Intersection over Union (Inner-CIoU) loss function is adopted to improve localization accuracy and training stability. Experimental results on the NEU-DET dataset demonstrate a 5.8% improvement in the mAP@0.5, a 2.4% improvement in the mAP@0.5:0.95, and a 6.2% improvement in the F1-score compared to the YOLOv11n baseline, with GFLOPs reduced to 6.1 and inference speed reaching 162.1 frames per second (FPS). Evaluations on the GC10-DET dataset, APSPC dataset, and a PCB defect dataset further confirm the generalization capability of YOLO-LSDI, with mAP@0.5 improvements of 4.2%, 2.1%, and 3.1%, and corresponding mAP@0.5:0.95 improvements of 1.1%, 1.5%, and 1.3%, respectively. These results validate the effectiveness and practicality of the proposed model for real-time industrial defect-detection tasks. Full article

Steel plays a fundamental role in modern smart city development, where its surface structural integrity is decisive for operational safety and long-term sustainability. While deep learning approaches show promise, their effectiveness remains limited by inadequate receptive field adaptability, suboptimal feature fusion strategies, and insufficient sensitivity to small defects. To overcome these limitations, we propose FAD-Net, a deep learning framework specifically designed for surface defect detection in steel materials within urban infrastructure. The network incorporates three key innovations: The RFCAConv module, which leverages dynamic receptive field construction and coordinate attention mechanisms to enhance feature representation for defects with long-range spatial dependencies and low-contrast characteristics. The MSDFConv module, employing multi-scale dilated convolutions with optimized dilation rates to preserve fine details while expanding the receptive field. An Auxiliary Head that introduces hierarchical supervision to improve the detection of small-scale defects. Experiments on the GC10-DET dataset showed that FAD-Net achieved 5.0% higher mAP@0.5 than baseline models. Cross-dataset validation with NEU and RDD2022 further confirmed its robustness. These results demonstrate FAD-Net’s effectiveness for automated infrastructure health monitoring. Full article

Surface defect detection on steel strips is a critical step in quality control for industrial products. While existing research has made some progress in optimizing annotation strategies and improving efficiency, issues such as feature aliasing during the annotation process, the insufficient utilization of boundary information, and the inaccurate representation of complex defect patterns remain inadequately addressed. To tackle these challenges, this paper proposes an annotation optimization framework from the perspective of feature analysis. The framework decomposes defect features into geometric features and grayscale distribution features and, based on feature decoupling theory, classifies defects into three typical patterns: block, linear, and textured defects. For each pattern, the minimum annotation units that preserved essential features were designed, enabling the standardized representation of complex defects and precise boundary localization. Experiments on the NEU-DET dataset showed that this annotation framework improves the average mAP of six mainstream detection models by 4.9 percentage points, validating its effectiveness in enhancing the detection performance. Additionally, this paper introduces an Efficiency–Cost Ratio (ECR) evaluation metric to quantify the relationship between the annotation cost and performance improvement. The study found that block and linear defect detection achieved optimal performance with only 50% annotation effort. This research not only improved the performance of defect detection models but also quantified the annotation resource utilization efficiency, providing robust theoretical support and practical guidance for efficient defect detection in complex industrial scenarios. Full article

Accurate detection of steel surface defects is crucial for ensuring safety and efficiency in steel production. In this study, we propose a multi-scale edge-enhanced structured composite detection Transformer (MESC-DETR) based on the RT-DETR framework for steel surface defect detection. Three primary improvements are introduced, as follows: (1) a Composite-ConvNeXtV2 backbone network architecture is developed, which integrates ConvNeXtV2 networks through a dense higher-level composition (DHLC) method to enhance multi-scale feature extraction capabilities; (2) an edge enhancement module (EEM) is proposed, incorporating a scale sequence feature fusion (SSFF) structure to design an edge-enhanced feature fusion (EEFF) architecture, thereby improving multi-scale defect detection and edge information perception; (3) a novel Focal-MPDIoU loss function is formulated by optimizing focal loss and MPDIoU, which further enhances model convergence speed and localization accuracy. Experimental results demonstrate that on GC10-DET and NEU-DET datasets, the proposed algorithm achieves 7.2% and 3.7% improvements in mean average precision (mAP) at IoU = 0.50, along with 2.9% and 1.5% mAP enhancements under IoU = 0.50:0.95. These findings indicate that MESC-DETR exhibits superior performance in steel surface defect detection, holding significant implications for steel manufacturing processes. Full article

The detection of surface defects in steel is a prerequisite for improving steel quality. When detecting surface defects in steel, the texture features of defective areas often show significant differences from the symmetry patterns of normal areas. To address the issues of low accuracy and slow recognition speed in existing steel surface defect detection methods, this study proposes an improved defect detection method based on YOLOv8s. To focus on the information of asymmetric areas in images and amplify the model’s capacity to learn target defects, we integrate the ODConv (Omni-Dimensional Dynamic Convolution) module into the backbone feature extraction network. This module infuses attention within the convolution process, augmenting the feature extraction capacity of the backbone network. Furthermore, to refine the regression speed of target boxes and enhance positioning accuracy, we adopt the WIoU (Wise Intersection over Union) bounding box loss function, featuring a dynamic non-monotonic focusing mechanism. Experimental results on the NEU-DET dataset reveal that the improved YOLOv8s-OD model achieves a 4.5% accuracy improvement compared to the original YOLOv8s, with an mAP of 78.9%. The model demonstrates robust performance in steel surface defect detection. With a modest size of only 21.5 MB, the model sustains a high detection speed of 89FPS, elevating detection accuracy while preserving real-time performance. This renders the model highly applicable in real-world industrial scenarios. Full article

At present, the detection of steel surface defects is still challenging, because there are some problems in steel products, such as complex background and noise interference, making it difficult to accurately detect complex small targets and great changes in defects at different scales, which directly affects product quality and endangers life safety. To solve the above problems, this paper proposes a steel surface defect detection network based on global attention perception and cross-layer interactive fusion, named GCF-Net. Firstly, this paper proposes an Interactive Feature Extraction Network (IFE-Net), which uses a local modeling feature extraction module to enhance the extraction of local detail features and uses a global attention perception module to capture the global contextual information in the image, thus improving the detection of complex background and noise defects. Secondly, this paper proposes a Cross-Layer Interactive Fusion Network (CIF-Net), which makes up for the fine-grained information lost during the gradual refinement of features through the fusion of adjacent layers, fully integrates shallow and deep features, and at the same time enhances the interaction between different scales by cross-layer fusion, thus improving the recognition ability of defect targets of different scales. Thirdly, the Interactive Fusion Module (IFM) is proposed, which can adjust the importance of each mosaic feature by attention to make efficient use of all feature information and improve the detection of complex background defects. Finally, in order to solve the problems of difficult positioning and inaccurate detection of small targets, this paper aims to strengthen the sensitive loss Q_IOU of small targets and improve the perception of complex small targets in steel defects. Compared with the baseline model, mAP@.5 is improved by 7.0%, 4.4%, and 2.5% on the NEU-DET, PCB, and Steel datasets, respectively, and it is better than all of the comparison models. Full article

Currently, in the domain of surface defect detection on hot-rolled strip steel, detecting small-target defects under complex background conditions and effectively balancing computational efficiency with detection accuracy presents a significant challenge. This study proposes CTL-YOLO based on YOLO11, aimed at efficiently and accurately detecting blemishes on the surface of hot-rolled strip steel in industrial applications. Firstly, the CGRCCFPN feature integration network is proposed to achieve multi-scale global feature fusion while preserving detailed information. Secondly, the TVADH Detection Head is proposed to identify defects under complex textured backgrounds. Finally, the LAMP algorithm is used to further compress the network. The proposed algorithm demonstrates excellent performance on the public dataset NEU-DET, achieving a mAP50 of 77.6%, representing a 3.2 percentage point enhancement compared to the baseline algorithm. The GFLOPs is reduced to 2.0, a 68.3% decrease compared to the baseline, and the Params are reduced to 0.40, showing an 84.5% reduction. Additionally, it exhibits strong generalization capabilities on the public dataset GC10-DET. The algorithm can effectively improve detection accuracy while maintaining a lightweight design. Full article

Small defects on the surface of copper strips have a significant impact on key properties such as electrical conductivity and corrosion resistance, and existing inspection techniques struggle to meet the demand in terms of accuracy and generalisability. Although there have been some studies on metal surface defect detection, there is a relative lack of research on highly reflective copper strips. In this paper, a lightweight and efficient copper strip defect detection algorithm, SC-AttentiveNet, is proposed, aiming to solve the problems of the large model size, slow speed, insufficient accuracy and poor generalisability of existing models. The algorithm is based on ConvNeXt V2, and combines the SCDown module and group normalisation to design the SCGNNet feature extraction network, which significantly reduces the computational overhead while maintaining excellent feature extraction capability. In addition, the algorithm introduces the SPPF-PSA module to enhance the multi-scale feature extraction capability, and constructs a new neck feature fusion network via the HD-CF Fusion Block module, which further enhances the feature diversity and fine granularity. The experimental results show that SC-AttentiveNet has a mAP of 90.11% and 64.14% on the KUST-DET and VOC datasets, respectively, with a parameter count of only 6.365 MB and a computational complexity of 14.442 GFLOPs. Tests on the NEU-DET dataset show that the algorithm has an excellent generalisation performance, with a mAP of 76.41% and a detection speed of 78 FPS, demonstrating a wide range of practical application potential. Full article

Surface defects during steel production can severely impact product quality and safety, making defect detection crucial. To improve the precision and performance of conventional approaches, we introduce FMV-YOLO, a model for detecting steel surface defects, built upon YOLOv11n. First, we substitute the C2PSA attention module in the backbone network with an Adaptive Fine-Grained Channel Attention (FCA) module, which improves defect type identification while reducing the parameter count. Next, we incorporate a new Multi-Scale Attention Fusion module (MSAF) to strengthen feature representation and refine the loss function using Normalized Wasserstein Distance (NWD) loss, thereby improving the localization accuracy of small defects. Finally, we integrate the VoV-GSCSP module within the neck network to achieve lightweighting, facilitating real-world deployment. Extensive experiments on the GC10DET and NEU-DET datasets demonstrate that the model effectively balances detection accuracy, parameter count, and computational load. With 2.6M parameters and 5.7G FLOPs, the model attains an mAP@0.5 of 73.4% on GC10DET and 80.2% on NEU-DET. Additionally, the method achieves 99% detection accuracy on a self-constructed industrial dataset, proving its effectiveness in industrial defect detection. Full article