Abstract
Visual perception has become a prerequisite for the operation of automated walnut vibration harvesting robots under complex orchard conditions. This study proposes an effective trunk target detection algorithm, TIoU-YOLOv8n-Pruned, based on YOLOv8n. First, to enhance the accuracy of walnut trunk prediction boxes matching true boxes at high overlap levels, a TIoU loss function is introduced. Second, to mitigate vibration effect caused by the PTO axis of the tractor, vibration trajectory fitting and coordinate correction are performed by capturing multiple images per second. To meet the frame rate requirements for coordinate correction, channel pruning removes 55% of the model’s non-essential channels. Experimental results show that the number of parameters and GFLOPs of TIoU-YOLOv8-Pruned are 950,000 and 3.6 GFLOPs, respectively, while its accuracy and mAP@0.5:0.95 reach 94.1% and 57.2%, outperforming YOLOv5n, YOLOv8n, YOLOv11n, FasterNet-YOLOv8n, Ghost-YOLOv8n, ShuffleNetv2-YOLOv8n, MobileNetV3-YOLOv8n, EfficientNet-YOLOv8n, GhostNetV3-YOLOv8n and MobileNetV4-YOLOv8n. After trajectory fitting and coordinate correction, it significantly reduces vibration-induced errors and enhances localization accuracy. Overall, the TIoU-YOLOv8n-Pruned model demonstrates applicability for trunk identification and localization in walnut orchard mechanical shaking harvesting, offering theoretical guidance for developing automated shaking harvesting equipment.
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Claims 6
Claim · model-written summary
TIoU-YOLOv8n-Pruned reached 94.1% accuracy and 57.2% mAP@0.5:0.95, outperforming YOLOv5n, YOLOv8n, YOLOv11n and other YOLO variants.
Verbatim
t0“Walnut Trunks”
e0“while its accuracy and mAP@0.5:0.95 reach 94.1% and 57.2%, outperforming YOLOv5n, YOLOv8n, YOLOv11n”
Claim · model-written summary
TIoU-YOLOv8n-Pruned has 950,000 parameters.
Verbatim
t0“Walnut Trunks”
e1“the number of parameters and GFLOPs of TIoU-YOLOv8-Pruned are 950,000 and 3.6 GFLOPs”
Claim · model-written summary
TIoU-YOLOv8n-Pruned requires 3.6 GFLOPs.
Verbatim
t0“Walnut Trunks”
e1“the number of parameters and GFLOPs of TIoU-YOLOv8-Pruned are 950,000 and 3.6 GFLOPs”
Claim · model-written summary
Channel pruning removed 55% of the model non-essential channels.
Verbatim
t0“Walnut Trunks”
e2“channel pruning removes 55% of the model’s non-essential channels”
Claim · model-written summary
Trajectory fitting and coordinate correction significantly reduced vibration-induced errors in walnut trunk localization.
- Taxon
- Walnutt0
- Treatment
- trajectory fitting and coordinate correctione3
- Outcome
- vibration-induced errorse3
- Direction
- reducese3
Not reportedComparator
Verbatim
t0“Walnut Trunks”
e3“After trajectory fitting and coordinate correction, it significantly reduces vibration-induced errors and enhances localization accuracy”
Claim · model-written summary
Trajectory fitting and coordinate correction enhanced localization accuracy of walnut trunks.
- Taxon
- Walnutt0
- Treatment
- trajectory fitting and coordinate correctione3
- Outcome
- localization accuracye3
- Direction
- enhancese3
Not reportedComparator
Verbatim
t0“Walnut Trunks”
e3“After trajectory fitting and coordinate correction, it significantly reduces vibration-induced errors and enhances localization accuracy”