Sep 2026· Signal, Image and Video Processing· Vol 20· 0 citations· 24 references
TL;DR
The proposed LKDT-YOLO is an advanced framework integrating three key innovations: a Dynamic Allocation-Partially Transformer Block that fuses locally ambiguous features with global contextual representations; an SPPF module with Large Scale Separable Kernel Attention (LSSKA) employing dynamic spatial attention to enhance discriminative edge feature extraction through expanded receptive fields; and a dedicated P2 detection layer preserving high-resolution spatial details for small targets.
Visual object detection is essential for environment perception in intelligent robots, automated assembly, unmanned inspection, and industrial detection systems. Although lightweight detectors reduce complexity through compact architectures, fixed convolutional units, and progressive downsampling, their limited scale r...
Xue-Bing Yue, Meng-Kui Hao, Yao Yao et al.· International Conference on...· 0 citations
A lightweight YOLO-style detector that integrates a PP-HGNetV2 tiny backbone, an enhanced normalization-based attention module (ImNAM), and an improved complete intersection-over-union loss (ImCIoU) shows strong potential for intelligent industrial inspection on resource-constrained platforms, subject to further hardwa...
Bangqiang Han, Qing Cheng, Sheng-Bin Wang et al.· Technologies· 0 citations
Visible–thermal object detection benefits from the complementary properties of RGB and thermal imagery, but repeated cross-modal fusion can increase model complexity, particularly in lightweight detectors. This paper proposes SDR-YOLO, a scale-selective detector designed to make better use of shallow spatial details wi...
Li-Juan Wang, Zu-Chao Bao, Bai-Chuan Rong et al.· Remote Sensing· 0 citations
An improved lightweight object detection model named GMFC-YOLO11n is proposed, based on YOLO11n, with systematic structural optimizations implemented to strengthen the joint modeling of local textures and global semantics, thereby improving the model’s perception of fine-grained down microstructures.
Precisely locating and counting highly dense workpieces on industrial assembly lines remains a critical challenge due to hyper-dense stacking distributions and intense surface specular reflections. To overcome these limitations, we propose GTDI-YOLO, an advanced single-stage detector that reconstructs multi-scale predi...
Wei-Bin Lin, Chao-Jun Dong, Cheng-Zhang Liang et al.· 2026 2nd International Confe...· 0 citations
A two-stage hand analysis framework that combines an optimized YOLOv5s detector with HRNet-based keypoint estimation and an adapted MSA-CAM module to improve feature representation in cluttered industrial scenes while reducing computational cost is presented.
Longxin Lin, Xin Wang, Deping Li· International journal of pat...· 0 citations
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