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IntroductionEfficiently 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.MethodsTo 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.ResultsExperimental 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.DiscussionThe 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. |
