Author

Hao Sun

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Open access Aug 2026

An optimized YOLOv11-based model for surface defect detection on valve stems in refrigeration equipment

Surface defect detection on industrial components remains challenging due to difficult feature extraction, low detection accuracy in complex backgrounds, and high computational demands. To address these challenges, this study presents the RDD-YOLO model based on the YOLOv11n architecture. The proposed model replaces specific convolutions in the backbone and neck with receptive field channel attention convolutions (RFCAConv) to enhance local target perception, and constructs a depth-adaptive kernel spatial pyramid pooling factorization (DAK_SPPF) module featuring DAKConv to modulate multi-scale feature fusion. To reduce computational complexity, a lightweight deep dynamic efficient (LDDE) detection head is structured, combining dynamic efficient local convolution (DELConv) and dynamic efficient depthwise convolution (DEWConv) to realize a lightweight Detect_DyHead architecture. The Wise Intersection over Union Version 3 (WIoU v3) loss function is introduced to optimize weight allocation for low-quality samples. Experimental results demonstrated that the proposed model achieved mAP@0.5 improvements of 5.7%, 4.4%, and 2.8% on the public NEU-DET, public DeepPCB, and custom valve stem datasets, respectively, thereby validating its efficacy for industrial defect detection.

Jiadong Dong, Feihu Sang, Hao Sun et al. · Scientific Reports · 0 citations

Jul 2026

Pseudo-Label-Guided Dual-Supervised Alignment for Bearing Fault Diagnosis

The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.

Hao Sun, Shijin Ren, Zhang-Qing Gu · SAE technical paper series · 0 citations