S-YOLOv10-SIC

To solve the problem of accurate recognition and picking of tea by tea picking robot, this study proposes a S-YOLOv10-SIC algorithm that integrates slice-assisted hyper-inference algorithm. This algorithm enhances the YOLOv10 network by introducing Space-to-Depth Convolution, asymptotic feature pyramid network, and Inner-IoU. These improvements reduce the loss of detailed information in long-distance and low-resolution images, improve key layer saliency, optimize non-adjacent layer fusion, enhance model convergence speed, and increase model universality. Experimental results demonstrate significant enhancements compared to YOLOv10, including a reduction of more than 30% in Bounding Box Regression Loss in the training set and a reduction of more than 60% in Classification Loss and Bounding Box Regression Loss in the verification set. Precision, Recall, and mAP also increased by 7.1%, 6.69%, and 6.78% respectively. Additionally, AP values for specific tea types saw improvements, with single bud, one bud and one leaf, and one bud and two leaves increasing by 6.10%, 7.99%, and 8.28% respectively. These advancements allow the improved model to effectively handle long-distance, small target, and low-resolution challenges while maintaining high precision and recall rates, laying the groundwork for the development of an Anji white tea picking robot.