YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection
Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension...
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MDPI AG
2025-03-01
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| author | Qi Zhou Huicheng Li Zhiling Cai Yiwen Zhong Fenglin Zhong Xiaoyu Lin Lijin Wang |
| author_facet | Qi Zhou Huicheng Li Zhiling Cai Yiwen Zhong Fenglin Zhong Xiaoyu Lin Lijin Wang |
| author_sort | Qi Zhou |
| collection | DOAJ |
| description | Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management. |
| format | Article |
| id | doaj-art-9d80205870b84059b6f00a035d57743f |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-9d80205870b84059b6f00a035d57743f2025-08-20T02:58:57ZengMDPI AGSensors1424-82202025-03-01255163510.3390/s25051635YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed DetectionQi Zhou0Huicheng Li1Zhiling Cai2Yiwen Zhong3Fenglin Zhong4Xiaoyu Lin5Lijin Wang6College 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 Horticulture, 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, ChinaEffective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.https://www.mdpi.com/1424-8220/25/5/1635weed detectiondeep learningYOLOv5sattention mechanism |
| spellingShingle | Qi Zhou Huicheng Li Zhiling Cai Yiwen Zhong Fenglin Zhong Xiaoyu Lin Lijin Wang YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection Sensors weed detection deep learning YOLOv5s attention mechanism |
| title | YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection |
| title_full | YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection |
| title_fullStr | YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection |
| title_full_unstemmed | YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection |
| title_short | YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection |
| title_sort | yolo ace enhancing yolo with augmented contextual efficiency for precision cotton weed detection |
| topic | weed detection deep learning YOLOv5s attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/5/1635 |
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