■在错综复杂的环境中有效而准确地识别西红柿对于推进番茄收获的自动化至关重要。当前的对象检测算法速度慢,对遮挡和小番茄的识别准确率低。
■为了增强在复杂环境中对西红柿的检测,提出了一种轻量级温室番茄目标检测模型S-YOLO,基于YOLOv8s的几个关键改进:(1)为YOLOv8s量身定制的轻量级GSConv_SlimNeck结构是创新的,显着减少模型参数,以优化模型颈部以进行轻量化模型采集。(2)设计了一种改进的α-SimSPPF结构,有效提高了番茄的检测精度。(3)提出了一种增强型β-SIoU算法,优化了训练过程,提高了重叠番茄识别的准确率。(4)集成了SE注意模块,以使模型能够捕获更具代表性的温室番茄特征,从而提高检测精度。
■实验结果表明,增强的S-YOLO模型显著提高了检测精度,实现轻量化模型设计,并表现出快速的检测速度。实验结果表明,S-YOLO模型显著提高了检测精度,达到96.60%的精度,平均精度(mAP)92.46%,和74.05FPS的检测速度,提高了5.25%,2.1%,和3.49FPS分别比原来的模型。模型参数仅为9.11M,S-YOLO优于CenterNet等模型,YOLOv3,YOLOv4,YOLOv5m,YOLOv7和YOLOv8,有效地解决了遮挡和小番茄的识别准确率低的问题。
■S-YOLO模型的轻巧特性使其适用于番茄采摘机器人的视觉系统,为基于移动边缘计算的设施环境下的机器人目标识别和收获操作提供技术支持。
UNASSIGNED: Efficiently and precisely identifying tomatoes amidst intricate surroundings is essential for advancing the automation of tomato harvesting. Current object detection algorithms are slow and have low recognition accuracy for occluded and small tomatoes.
UNASSIGNED: To enhance the detection of tomatoes in complex environments, a
lightweight greenhouse tomato object detection model named S-YOLO is proposed, based on YOLOv8s with several key improvements: (1) A
lightweight GSConv_SlimNeck structure tailored for YOLOv8s was innovatively constructed, significantly reducing model parameters to optimize the model neck for
lightweight model acquisition. (2) An improved version of the α-SimSPPF structure was designed, effectively enhancing the detection accuracy of tomatoes. (3) An enhanced version of the β-SIoU algorithm was proposed to optimize the training process and improve the accuracy of overlapping tomato recognition. (4) The SE attention module is integrated to enable the model to capture more representative greenhouse tomato features, thereby enhancing detection accuracy.
UNASSIGNED: Experimental results demonstrate that the enhanced S-YOLO model significantly improves detection accuracy, achieves
lightweight model design, and exhibits fast detection speeds. Experimental results demonstrate that the S-YOLO model significantly enhances detection accuracy, achieving 96.60% accuracy, 92.46% average precision (mAP), and a detection speed of 74.05 FPS, which are improvements of 5.25%, 2.1%, and 3.49 FPS respectively over the original model. With model parameters at only 9.11M, the S-YOLO outperforms models such as CenterNet, YOLOv3, YOLOv4, YOLOv5m, YOLOv7, and YOLOv8s, effectively addressing the low recognition accuracy of occluded and small tomatoes.
UNASSIGNED: The
lightweight characteristics of the S-YOLO model make it suitable for the visual system of tomato-picking robots, providing technical support for robot target recognition and harvesting operations in facility environments based on mobile edge computing.