YOUNG APPLE FRUITS DETECTION METHOD BASED ON IMPROVED YOLOV5

Abstract

The intelligent detection of young apple fruits based on deep learning faced various challenges such as varying scale sizes and colors similar to the background, which increased the risk of misdetection or missed detection. To effectively address these issues, a method for young apple fruit detection based on improved YOLOv5 was proposed in this paper. Firstly, a young apple fruits dataset was established. Subsequently, a prediction layer was added to the detection head of the model, and four layers of CA attention mechanism were integrated into the detection neck (Neck). Additionally, the GIOU function was introduced as the model's loss function to enhance its overall detection performance. The accuracy on the validation dataset reached 94.6%, with an average precision of 82.2%. Compared with YOLOv3, YOLOv4, and the original YOLOv5 detection methods, the accuracy increased by 0.4%, 1.3%, and 4.6% respectively, while the average precision increased by 0.9%, 1.6%, and 1.2% respectively. The experiments demonstrated that the algorithm effectively recognized young apple fruits in complex scenes while meeting real-time detection requirements, providing support for intelligent apple orchard management.