PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection
Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the...
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2025-01-01
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| author | Zhou Wang Yuting Su Feng Kang Lijin Wang Yaohua Lin Qingshou Wu Huicheng Li Zhiling Cai |
| author_facet | Zhou Wang Yuting Su Feng Kang Lijin Wang Yaohua Lin Qingshou Wu Huicheng Li Zhiling Cai |
| author_sort | Zhou Wang |
| collection | DOAJ |
| description | Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model—PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection. By extracting the feature information of small objects in the high-resolution stage of the image, the P2 layer helps the network better capture small objects. At the same time, in order to reduce unnecessary calculations and lower the complexity of the model, we removed the P5 layer. In addition, we have introduced the coordinate spatial attention mechanism, which can help the network more accurately obtain the spatial and positional features required for small targets, thereby further improving detection accuracy. In the VisDrone2019 datasets, experimental results show that PC-YOLO11s outperforms other existing YOLO-series models in overall performance. Compared with the baseline YOLO11s model, PC-YOLO11s mAP@0.5 increased from 39.5% to 43.8%, mAP@0.5:0.95 increased from 23.6% to 26.3%, and the parameter count decreased from 9.416M to 7.103M. Not only that, we also applied PC-YOLO11s to tea bud datasets, and experiments showed that its performance is superior to other YOLO-series models. Experiments have shown that PC-YOLO11s exhibits excellent performance in small object detection tasks, with strong accuracy improvement and good generalization ability, which can meet the needs of small object detection in practical applications. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-393ae17795aa4e0c9e634cda99d304c62025-01-24T13:48:36ZengMDPI AGSensors1424-82202025-01-0125234810.3390/s25020348PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image DetectionZhou Wang0Yuting Su1Feng Kang2Lijin Wang3Yaohua Lin4Qingshou Wu5Huicheng Li6Zhiling Cai7College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaSchool of Mathematics and Computer Science, Wuyi University, Wuyishan 354300, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCompared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model—PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection. By extracting the feature information of small objects in the high-resolution stage of the image, the P2 layer helps the network better capture small objects. At the same time, in order to reduce unnecessary calculations and lower the complexity of the model, we removed the P5 layer. In addition, we have introduced the coordinate spatial attention mechanism, which can help the network more accurately obtain the spatial and positional features required for small targets, thereby further improving detection accuracy. In the VisDrone2019 datasets, experimental results show that PC-YOLO11s outperforms other existing YOLO-series models in overall performance. Compared with the baseline YOLO11s model, PC-YOLO11s mAP@0.5 increased from 39.5% to 43.8%, mAP@0.5:0.95 increased from 23.6% to 26.3%, and the parameter count decreased from 9.416M to 7.103M. Not only that, we also applied PC-YOLO11s to tea bud datasets, and experiments showed that its performance is superior to other YOLO-series models. Experiments have shown that PC-YOLO11s exhibits excellent performance in small object detection tasks, with strong accuracy improvement and good generalization ability, which can meet the needs of small object detection in practical applications.https://www.mdpi.com/1424-8220/25/2/348small object detectionYOLO11attention mechanismVisDrone2019tea bud |
| spellingShingle | Zhou Wang Yuting Su Feng Kang Lijin Wang Yaohua Lin Qingshou Wu Huicheng Li Zhiling Cai PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection Sensors small object detection YOLO11 attention mechanism VisDrone2019 tea bud |
| title | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection |
| title_full | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection |
| title_fullStr | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection |
| title_full_unstemmed | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection |
| title_short | PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection |
| title_sort | pc yolo11s a lightweight and effective feature extraction method for small target image detection |
| topic | small object detection YOLO11 attention mechanism VisDrone2019 tea bud |
| url | https://www.mdpi.com/1424-8220/25/2/348 |
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