Hybrid attention transformer integrated YOLOV8 for fruit ripeness detection.

The complexity of the outdoor orchard environment, especially the changes in light intensity and the shadows generated by fruit clusters, present challenges in the identification and classification of mature fruits. To solve these problems, this paper proposes an innovative fruit recognition model, HAT-YOLOV8, aiming to combine the advantages of Hybrid Attention Transformer (HAT) and YOLOV8 deep learning algorithm. This model improves the ability to capture complex dependencies by integrating the Shuffle Attention (SA) module while maintaining low computational complexity. In addition, during the feature fusion stage, the Hybrid Attention Transformer (HAT) module is integrated into TopDownLayer2 to enhance the capture of long-term dependencies and the recovery of detailed information in the input data. To more accurately evaluate the similarity between the prediction box and the real bounding box, this paper uses the EIoU loss function instead of CIoU, thereby improving detection accuracy and accelerating model convergence. In terms of evaluation, this study was experimented on a dataset containing five fruit varieties, each of which was classified into three different maturity levels. The results show that the HAT-YOLOV8 model improved mAP by 11%, 10.2%, 7.6% and 7.8% on the test set, and the overall mAP reached 88.9% respectively. In addition, the HAT-YOLOV8 model demonstrates excellent generalization capabilities, indicating its potential for application in the fields of fruit recognition, maturity assessment and fruit picking automation.