Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n
The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1938 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849340668874326016 |
|---|---|
| author | Lili Song Haixin Deng Jianfeng Han Xiongwei Gao |
| author_facet | Lili Song Haixin Deng Jianfeng Han Xiongwei Gao |
| author_sort | Lili Song |
| collection | DOAJ |
| description | The water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. To address the aforementioned challenges, we proposed an improved YOLOv8-HSH algorithm based on YOLOv8n. The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. Experimental results indicate that the proposed strategy yields a significant enhancement, with mAP50 and mAP50-95 increasing by 11.7% and 12.4%, respectively, while the miss rate decreases by 11%. The F1 score has increased by 11%, and the average accuracy for each category of floating objects has enhanced by a minimum of 5.6%. These improvements not only significantly enhanced the model’s detection accuracy and robustness in complex scenarios but also provided new solutions for research in aerial image processing and related environmental monitoring fields. |
| format | Article |
| id | doaj-art-49f3cc37a5c7499082f08f22d222f343 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-49f3cc37a5c7499082f08f22d222f3432025-08-20T03:43:51ZengMDPI AGSensors1424-82202025-03-01256193810.3390/s25061938Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8nLili Song0Haixin Deng1Jianfeng Han2Xiongwei Gao3School of Information Engineering, Inner Mongolia University of Technology, Jinchuan Campus, Hohhot 010080, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Jinchuan Campus, Hohhot 010080, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Jinchuan Campus, Hohhot 010080, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Jinchuan Campus, Hohhot 010080, ChinaThe water surface environment is highly complex, and floating objects in aerial images often occupy a minimal proportion, leading to significantly reduced feature representation. These challenges pose substantial difficulties for current research on the detection and classification of water surface floating objects. To address the aforementioned challenges, we proposed an improved YOLOv8-HSH algorithm based on YOLOv8n. The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. Experimental results indicate that the proposed strategy yields a significant enhancement, with mAP50 and mAP50-95 increasing by 11.7% and 12.4%, respectively, while the miss rate decreases by 11%. The F1 score has increased by 11%, and the average accuracy for each category of floating objects has enhanced by a minimum of 5.6%. These improvements not only significantly enhanced the model’s detection accuracy and robustness in complex scenarios but also provided new solutions for research in aerial image processing and related environmental monitoring fields.https://www.mdpi.com/1424-8220/25/6/1938aerial photographsmall object detectionfloating object recognitionenvironmental monitoring |
| spellingShingle | Lili Song Haixin Deng Jianfeng Han Xiongwei Gao Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n Sensors aerial photograph small object detection floating object recognition environmental monitoring |
| title | Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n |
| title_full | Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n |
| title_fullStr | Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n |
| title_full_unstemmed | Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n |
| title_short | Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n |
| title_sort | improved aerial surface floating object detection and classification recognition algorithm based on yolov8n |
| topic | aerial photograph small object detection floating object recognition environmental monitoring |
| url | https://www.mdpi.com/1424-8220/25/6/1938 |
| work_keys_str_mv | AT lilisong improvedaerialsurfacefloatingobjectdetectionandclassificationrecognitionalgorithmbasedonyolov8n AT haixindeng improvedaerialsurfacefloatingobjectdetectionandclassificationrecognitionalgorithmbasedonyolov8n AT jianfenghan improvedaerialsurfacefloatingobjectdetectionandclassificationrecognitionalgorithmbasedonyolov8n AT xiongweigao improvedaerialsurfacefloatingobjectdetectionandclassificationrecognitionalgorithmbasedonyolov8n |