YOLO-CSB: A Model for Real-Time and Accurate Detection and Localization of Occluded Apples in Complex Orchard Environments

摘要

Apples are cultivated over a large global area with high yields, and efficient robotic harvesting requires accurate detection and localization, particularly in complex orchard environments where occlusion by leaves and fruits poses substantial challenges. To address this, we proposed a YOLO-CSB model-based method for apple detection and localization, designed to overcome occlusion and enhance the efficiency and accuracy of mechanized harvesting. Firstly, a comprehensive apple dataset was constructed, encompassing various lighting conditions and leaf obstructions, to train the model. Subsequently, the YOLO-CSB model, built upon YOLO11s, was developed with improvements including the integration of a lightweight CSFC Block to reconstruct the backbone, making the model more lightweight; the SEAM component is introduced to improve feature restoration in areas with occlusions, complemented by the efficient BiFPN approach to boost detection precision. Additionally, a 3D positioning technique integrating YOLO-CSB with an RGB-D camera is presented. Validation was conducted via ablation analyses, comparative tests, and 3D localization accuracy assessments in controlled laboratory and structured orchard settings, The YOLO-CSB model demonstrated effectiveness in apple target recognition and localization, with notable advantages under leaf and fruit occlusion conditions. Compared to the baseline YOLO11s model, YOLO-CSB improved mAP by 3.02% and reduced the parameter count by 3.19%. Against mainstream object detection models, YOLO-CSB exhibited significant advantages in detection accuracy and model size, achieving a mAP of 93.69%, precision of 88.82%, recall of 87.58%, and a parameter count of only 9.11 M. The detection accuracy in laboratory settings reached 100%, with average localization errors of 4.15 mm, 3.96 mm, and 4.02 mm in the X, Y, and Z directions, respectively. This method effectively addresses complex occlusion environments, enabling efficient detection and precise localization of apples, providing reliable technical support for mechanized harvesting.