Rapid Identification and Accurate Localization of Walnut Trunks Based on TIoU-YOLOv8n-Pruned

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.