A deep learning approach for fruit detection: YOLO-GF

Abstract

To achieve automatic fruit object recognition in complex backgrounds, this paper proposes a fruit object detection algorithm based on YOLO-GF. Addressing challenges such as complex backgrounds, significant variations in target shapes, and instances of occlusion in fruit images, we utilize the Global Attention Mechanism (GAM) to enhance the feature extraction capability for fruit targets, thereby improving fruit recognition accuracy. Additionally, the Focal-EIOU loss function is used instead of the CIOU loss function to expedite model convergence. Experimental results demonstrate a significant improvement in recognition accuracy under the same hardware conditions. On the same test dataset, the improved model achieves an mAP50 of 92.1% and mAP50:95 of 76.5%, representing increases of 5.8% and 11.9% compared to the original model, respectively.