Jul 2026· Engineering Research Express· Vol 8, pp. 155404· 0 citations· 18 references
Physics
Abstract
To address the challenges of weak feature representation, background interference, and missed small-target detections in identifying subtle surface damages on highly reflective refrigerator doors during production, this paper proposes a lightweight detection algorithm, YOLOv8-FD, based on the YOLOv8n architecture. First, a lightweight GhostConv module replaces standard convolution layers within the backbone network. This is integrated with a modified lightweight CBAM attention mechanism (L-CBAM) to form the C2f-GC module, significantly enhancing fine-grained feature extraction under complex backgrounds while minimising parametric overhead. Furthermore, the SPPELAN module is incorporated to expand the receptive field and aggregate multi-scale features without compromising the lightweight design. To adapt to the geometric heterogeneity of defects, a novel Static-Asymmetry IoU (SAIoU) loss function based on a subtraction penalty mechanism is proposed, which optimises bounding box regression for irregular and asymmetric targets. Experimental evaluations on a strictly partitioned, independent hold-out test set demonstrate that YOLOv8-FD operates with only 5.3 M parameters and 6.0 GFLOPs of computational complexity, achieving reductions of 15.9% and 26.8%, respectively, compared to the baseline YOLOv8n model. Concurrently, it improves the mean average precision (mAP50) by 1.33 percentage points and the F1-score by 1.10 percentage points. These quantitative results empirically validate the efficacy of YOLOv8-FD in resolving fine-grained damage detection bottlenecks under resource-constrained edge-device scenarios.
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
Lightweight detectors for steel surface defects still struggle to balance robust feature representation with deployment efficiency when defects exhibit weak textures, fine details, and large variation in scale and shape. To narrow this gap, this study develops an incremental lightweight enhancement of YOLO11n rather than proposing a brand-new detection framework. The method combines two coordinated changes. First, an improved lightweight channel-aware fusion module with a dilated branch, termed LCAF-D, is inserted into the neck to strengthen context-aware multi-scale aggregation at low additional cost. Second, a hierarchical Mish activation strategy is introduced only in selected deep backbone layers and neck downsampling layers so that nonlinear modeling is strengthened without disturbing shallow low-level feature extraction. Experiments on NEU-DET indicate that the proposed design provides competitive lightweight detection performance. The benefit becomes more evident as the input resolution increases. Under the 640 × 640 setting, YOLO11n + LCAF-D + Mish reaches 77.1% mAP@0.5 and 44.3% mAP@0.5:0.95, exceeding the YOLO11n baseline. Five-seed repeated experiments further show slightly better mean accuracy with stable variation, and runtime benchmarking shows that the method keeps lightweight characteristics with only modest increases in parameters, GFLOPs, latency, and GPU memory. Additional evaluation on GC10-DET under the same 640 × 640 protocol also gives an overall improvement over the baseline, although the gains remain category-dependent rather than universal. Overall, the method is best understood as a competitive lightweight engineering refinement for steel surface defect detection, particularly when the input resolution is sufficient to preserve subtle defect details.
The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.
Aobo Yue, Puchun Chen, Yan Yang· Journal of Real-Time Image P...· 0 citations
Insulators play a vital role in ensuring the safe and stable operation of transmission lines. This study develops IDD-YOLO, an engineering-oriented lightweight detector for UAV-based insulator inspection, with emphasis on reducing model complexity while preserving the weak visual information of localized defects. GhostConv is used to reduce redundant computation in the backbone and neck, while a GhostConv–CARAFE lightweight neck combines efficient feature transformation with content-aware upsampling to preserve fine-grained defect information. EIoU is further employed as the bounding-box regression objective during training without adding inference-time network layers. On the IDID-Plus dataset, IDD-YOLO achieves a Precision of 83.9%, a Recall of 64.2%, and an mAP@0.5 of 66.3%, while requiring 4.2 M parameters and 9.6 GFLOPs. Compared with YOLOv11s, Precision, Recall, and mAP@0.5 increase by 4.3, 4.8, and 1.5 percentage points, respectively, whereas the parameter count and GFLOPs decrease by 54.3% and 41.5%. Although mAP@0.5:0.95 decreases slightly, the results demonstrate a competitive engineering-oriented trade-off between detection sensitivity and model complexity. The current study provides model-level evidence of lightweight design; practical deployment performance on UAV-compatible embedded hardware remains to be evaluated.
Insulators and their accessories are core components ensuring the safe operation of transmission lines. Defect detection for them is confronted with challenges such as small targets, complex backgrounds and difficulty in fitting the irregular boundaries of defects, while traditional lightweight algorithms suffer from insufficient detection performance. To tackle these challenges, this work presents a lightweight detection algorithm built upon the improved YOLOv11n: the DCNv3 (Deformable Convolution v3) is embedded into the backbone network to replace the traditional convolutional layers, enhancing the ability to extract features of irregular defects; the Inner-WIoU(Inner-Weighted Intersection over Union) loss function is employed to refine the bounding box regression. All experiments are conducted on a self-built dataset with 7,972 images and 7 defect categories. The proposed method achieves a mAP@50 of 86.4%, 3.2 percentage points higher than the baseline YOLOv11n, and outperforms mainstream lightweight detectors and small-object-oriented detectors. Meanwhile, the algorithm retains the lightweight advantage and meets the needs of on-site real-time inspection. The model maintains a lightweight structure with only 2.6 M parameters and achieves an inference speed of 38 FPS on NVIDIA RTX 4080 Super, meeting real-time inspection requirements. Meanwhile, the algorithm retains the lightweight advantage and can meet the requirements of on-site real-time inspection. Since validation is limited to a self-constructed dataset, cross-scene testing and public benchmark evaluation will be conducted in future work to further verify the model’s robustness and generalization in real transmission-line inspection scenarios.
Yu-Jie Sheng, Junjie Sang, Liutao Lu· Digital Signal and Computer...· 0 citations
To address the challenges of insufficient feature extraction, limited multi-scale defect detection capability, and poor localization accuracy in steel surface defect detection tasks, this paper proposes an enhanced BCD-YOLOv11 detection algorithm based on YOLOv11. The algorithm incorporates three key improvements: First, the CARAFE upsampling operator is introduced in the feature fusion stage, replacing traditional interpolation methods with a content-aware feature reorganization mechanism. This significantly enhances the upsampling quality of feature maps and improves the retention of fine defect features. Second, the BiFPN network is adopted to reconstruct the multi-scale feature fusion path. Through weighted bidirectional cross-scale connections, it enables efficient integration of deep and shallow features, thereby effectively enhancing the model's detection performance for defects of varying sizes. Finally, the Wasserstein Distance Loss is employed as the bounding box regression loss function. By directly minimizing the distribution distance between predicted and ground truth boxes, it improves the localization accuracy of defects in complex backgrounds. Compared to the baseline YOLOv11 model, BCD-YOLOv11 achieves a 7.7% increase in precision and a 2.1% improvement in mAP while maintaining inference speed. It shows particularly notable performance in detecting small and overlapping defects, demonstrating the effectiveness of the proposed enhancements.
Gang Wang, Qianjun Ma, Dengshuai Li· International Conference on...· 0 citations
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