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Ping Wu

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Open access Aug 2026

SDM-YOLO: an improved YOLO model with multiscale attention for steel surface defect detection

Surface defects generated during steel manufacturing significantly affect product quality, structural reliability, and operational safety, creating a strong demand for accurate and real-time inspection systems in industrial environments. However, existing detection approaches often struggle with subtle defect textures, complex surface backgrounds, weak visual contrast, and the trade-off between detection accuracy and computational efficiency. To address these challenges, this paper proposes SDM-YOLO, a lightweight framework for steel surface defect detection based on YOLO11n. Rather than introducing an entirely new detection architecture, the main contribution of this work lies in the coordinated integration and task-specific adaptation of complementary modules within a unified lightweight detection framework. Specifically, the proposed method enhances feature representation by replacing the original C2PSA module with C2PSA_SEAM in the backbone, introduces DySample-based dynamic upsampling in the neck for content-aware multi-scale feature alignment, and incorporates a multi-scale convolutional attention mechanism before the detection head to improve sensitivity to subtle, low-contrast, and morphologically varied defects. In addition, a normalized Wasserstein distance loss is employed to improve localization stability for small and overlapping defects without increasing inference-time parameters or computational cost. Extensive experiments on the NEU-DET and GC10-DET datasets demonstrate that SDM-YOLO achieves mAP50 scores of 81.0% and 72.3%, respectively, while attaining mAP50:95 values of 46.7% and 38.0%. The proposed framework maintains real-time performance with only 2.68 M parameters, 6.6 GFLOPs, and an inference speed of 94.5 FPS. These results demonstrate that SDM-YOLO achieves an effective balance between detection accuracy, localization precision, and computational efficiency, making it suitable for practical steel surface defect inspection applications.

Nabin Kandel, Ping Wu · Engineering Research Express · 0 citations

Open access Jul 2026

SMG-YOLO: an efficient human pose estimation method

Human pose estimation is a fundamental task in computer vision that aims to localize key human joints in images. Although you only look once (YOLO)-based pose estimation methods provide advantages in computational efficiency and deployment convenience, they still face challenges in complex backgrounds, occlusions, small-scale keypoints, and structural inconsistency among predicted joints. To address these issues, this paper presents SMG-YOLO, an enhanced YOLO-based pose estimation framework built upon Hyper-YOLO-Pose. The proposed model integrates three pose-oriented components: the selective boundary aggregation (SBA) module, the mixed aggregation network (MANet)-StarC module, and a GroupNorm-based pose detection head, namely group normalization (GN)-Pose. The SBA module is adopted to strengthen semantic-spatial feature interaction between high-level semantic features and low-level spatial cues. The MANet-StarC module incorporates the StarC unit into the MANet structure and combines nonlinear feature modulation with context anchor attention to enhance contextual feature representation. GN-Pose introduces GN into the pose prediction head to improve keypoint regression stability. Experiments on the MPII human pose dataset show that SMG-YOLO achieves 85.30% AP50 and 47.70% AP50:95, improving the Hyper-YOLO-Pose baseline by 2.30 and 2.40 percentage points, respectively. Additional ablation experiments further verify the complementary effects of the proposed components. Moreover, model-forward speed testing on an RTX 3060 GPU shows that SMG-YOLO achieves 49.1 FPS, indicating practical inference efficiency under the tested setting. These results demonstrate that SMG-YOLO provides a favorable accuracy–complexity trade-off for human pose estimation.

Haiyun Zhou, Yicheng Yu, Ping Wu · Engineering Research Express · 0 citations