Improved model MASW YOLO for small target detection in UAV images based on YOLOv8
Abstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. This model aims to address the problems of small targets, missed detection, and misdetection of UAV viewpoint feature detection targets. The backbone network structure is enhanced by incorporating a multi-sca...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10428-w |
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| author | Xianghe Meng Fei Yuan Dexiang Zhang |
| author_facet | Xianghe Meng Fei Yuan Dexiang Zhang |
| author_sort | Xianghe Meng |
| collection | DOAJ |
| description | Abstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. This model aims to address the problems of small targets, missed detection, and misdetection of UAV viewpoint feature detection targets. The backbone network structure is enhanced by incorporating a multi-scale convolutional MSCA attention mechanism, which introduces a deep convolution process to aggregate local information. This method aims to increase small-target detection accuracy. Concurrently, the neck network structure is reconstructed, with the fusion effect of multi-scale weakening of non-adjacent levels addressed by using an AFPN progressive pyramid network to replace the PANFPN structure of the base model. The MSCA and AFPN form a multiscale feature synergy mechanism, whereby the response values of MSCA become inputs to AFPN, and the multiscale integration of AFPN further amplifies the advantages of MSCA. The use of flexible non-maximum suppression Soft-NMS is chosen to replace the non-maximum suppression NMS to improve the model’s detection of occlusion and dense targets. The loss function of the model is optimised through the implementation of Wise-IoU, which serves as a replacement for the loss function of the baseline model, thereby enhancing the accuracy of bounding box regression, especially perform better when the target deformation or scale change is large. Experiments conducted on the VisDrone2019 dataset demonstrate that the average detection accuracy of the MASW-YOLO algorithm is 38.3%, which is augmented by 7.9% through the utilisation of the original YOLOv8n network. Furthermore, the number of network parameters is reduced by 19.6%. |
| format | Article |
| id | doaj-art-c152ac7e3a6e47cbac9c52ca6266e41b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-c152ac7e3a6e47cbac9c52ca6266e41b2025-08-20T03:42:49ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-10428-wImproved model MASW YOLO for small target detection in UAV images based on YOLOv8Xianghe Meng0Fei Yuan1Dexiang Zhang2College of Electrical Engineering and Automation, Anhui UniversityInstitute of Information Engineering, Chinese Academy of SciencesCollege of Electrical Engineering and Automation, Anhui UniversityAbstract The present paper proposes an algorithmic model, MASW-YOLO, that improves YOLOv8n. This model aims to address the problems of small targets, missed detection, and misdetection of UAV viewpoint feature detection targets. The backbone network structure is enhanced by incorporating a multi-scale convolutional MSCA attention mechanism, which introduces a deep convolution process to aggregate local information. This method aims to increase small-target detection accuracy. Concurrently, the neck network structure is reconstructed, with the fusion effect of multi-scale weakening of non-adjacent levels addressed by using an AFPN progressive pyramid network to replace the PANFPN structure of the base model. The MSCA and AFPN form a multiscale feature synergy mechanism, whereby the response values of MSCA become inputs to AFPN, and the multiscale integration of AFPN further amplifies the advantages of MSCA. The use of flexible non-maximum suppression Soft-NMS is chosen to replace the non-maximum suppression NMS to improve the model’s detection of occlusion and dense targets. The loss function of the model is optimised through the implementation of Wise-IoU, which serves as a replacement for the loss function of the baseline model, thereby enhancing the accuracy of bounding box regression, especially perform better when the target deformation or scale change is large. Experiments conducted on the VisDrone2019 dataset demonstrate that the average detection accuracy of the MASW-YOLO algorithm is 38.3%, which is augmented by 7.9% through the utilisation of the original YOLOv8n network. Furthermore, the number of network parameters is reduced by 19.6%.https://doi.org/10.1038/s41598-025-10428-wDeep learningYOLOv8nMSCA attentionAFPNSoft-NMSWise-IoU |
| spellingShingle | Xianghe Meng Fei Yuan Dexiang Zhang Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 Scientific Reports Deep learning YOLOv8n MSCA attention AFPN Soft-NMS Wise-IoU |
| title | Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 |
| title_full | Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 |
| title_fullStr | Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 |
| title_full_unstemmed | Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 |
| title_short | Improved model MASW YOLO for small target detection in UAV images based on YOLOv8 |
| title_sort | improved model masw yolo for small target detection in uav images based on yolov8 |
| topic | Deep learning YOLOv8n MSCA attention AFPN Soft-NMS Wise-IoU |
| url | https://doi.org/10.1038/s41598-025-10428-w |
| work_keys_str_mv | AT xianghemeng improvedmodelmaswyoloforsmalltargetdetectioninuavimagesbasedonyolov8 AT feiyuan improvedmodelmaswyoloforsmalltargetdetectioninuavimagesbasedonyolov8 AT dexiangzhang improvedmodelmaswyoloforsmalltargetdetectioninuavimagesbasedonyolov8 |