Prof. Hui Li Profile

Prof. Hui Li

at Qingdao Univ of Science And Technology

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Publications (2)

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Proceedings Article | 3 January 2025 Paper

Low-altitude target detection algorithm for intelligent scenic areas based on improved YOLOv10

Xiao Li, Ji Sun, Pei Li, Ye Tao, Hui Li

Proceedings Volume 13519, 1351902 (2025) https://doi.org/10.1117/12.3058111

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With the development of drone technology, its application in intelligent scenic areas provides a new solution for tourist flow monitoring. To enhance detection accuracy and satisfy real-time demands, this study proposed a low-altitude target detection algorithm of intelligent scenic areas based on improved YOLOv10, and developed an intelligence scenic areas tourist flow monitoring and statistic system accordingly. By introducing the Large Separable Kernel Attention (LSKA) mechanism, the algorithm optimizes the Spatial Pyramid Pooling Fast (SPPF) module and effectively capturing long-range dependencies in images. In addition, we added a Small Target Detection Layer(STDL) to the YOLOv10 network structure to retain more location information and detailed features about small targets. Results from experiments conducted on the VisDrone2019 dataset show that, compared to the original YOLOv10 model, the enhanced version demonstrates an improvement in Recall by 2.0% and an increase in mAP@0.5 by 1.7%. Compared with other mainstream models, our proposed algorithm has improved on many evaluation metrics, and fulfills the requirements for real-time detection. It has been successfully applied to Tsingtao Beer Museum and has achieved good results. The results of the experiments indicate that the algorithm performs well in detecting low-altitude aerial photography images of drones, and provides effective technical assistance for the safety management of intelligent scenic areas.

Proceedings Article | 12 October 2022 Paper

Deformation convolution and self-attention for fabric defect detection

Xiangcong Lv, Hui Li, Beibei Shen, Yifan Wu

Proceedings Volume 12342, 123420J (2022) https://doi.org/10.1117/12.2644656

KEYWORDS: Defect detection, Convolution, Feature extraction, Detection and tracking algorithms, Image fusion

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Accurate and fast detection of fabric defects is of great significance to improve the production efficiency of textile enterprises. However, fabric defects have problems such as large-scale changes, small objects, and unbalanced numbers. Therefore, a fabric detection method integrating deformation convolution and self-attention is proposed. The algorithm effectively alleviates the problem of the model's insufficient ability to extract irregular flaw features by combining multiscale feature extraction with deformation convolution; At the same time, combined with the self-attention mechanism, a dual-channel feature fusion is designed, and adaptive adjustment and fusion are performed to obtain more effective features to make up for the low detection rate of small object defects. Finally, an adaptive bounding box generator is designed in the region proposal network to obtain more accurate object bounding boxes for subsequent detection and regression. Experimental results show that the proposed method has a good detection effect, and effectively improves the accuracy and efficiency of fabric defect defection.

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