AAB-YOLO: An Improved YOLOv11 Network for Apple Detection in Natural Environments

Apple detection in natural environments is crucial for advancing agricultural automation. However, orchards often employ bagging techniques to protect apples from pests and improve quality, which introduces significant detection challenges due to the varied appearance and occlusion of apples caused by bags. Additionally, the complex and variable natural backgrounds further complicate the detection process. To address these multifaceted challenges, this study introduces AAB-YOLO, a lightweight apple detection model based on an improved YOLOv11 framework. AAB-YOLO incorporates ADown modules to reduce model complexity, the C3k2_ContextGuided module for enhanced understanding of complex scenes, and the Detect_SEAM module for improved handling of occluded apples. Furthermore, the Inner_EIoU loss function is employed to boost detection accuracy and efficiency. The experimental results demonstrate significant improvements: mAP@50 increases from 0.917 to 0.921, precision rises from 0.948 to 0.951, and recall improves by 1.04%, while the model’s parameter count and computational complexity are reduced by 37.7% and 38.1%, respectively. By achieving lightweight performance while maintaining high accuracy, AAB-YOLO effectively meets the real-time apple detection needs in natural environments, overcoming the challenges posed by orchard bagging techniques and complex backgrounds.