# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 1 Introduction
[18] introduced an intelligent system that uses Faster R-CNN to detect strawberries in field scenarios, which achieved high accuracy for the automatic monitoring and harvesting of strawberries. While these R-CNN-based models demonstrated high detection accuracies, their two-stage processing typically led to slower operation speeds, averaging around 15 FPS. To balance detection accuracy and speed, single-stage detection algorithms have been widely employed in strawberry detection. The YOLO series is a typical one-stage detection algorithm with a speed advantage achieved by directly predicting classifications and bounding boxes. [PERSON] et al. [19] proposed a DSE (detailed semantics enhancement) model based on YOLOv3 for multi-stage strawberry detection, which excelled in identifying different strawberry stages, with an average precision of 86.58%. However, the model's large size, with 300 million parameters, resulted in a slower detection speed of just 18 FPS and hindered deployment on embedded devices. [PERSON] et al. [8] proposed a DSW-YOLO(DCNv3-SA-WIoU-YOLO) network model for detecting strawberries at different occlusion levels, which achieved an average precision of 86.7%. [PERSON] et al. [20] introduced a YOLOv3-based method for classifying strawberry maturity in UAV (unmanned aerial vehicle) images, which achieved an average accuracy of 88%. The aforementioned YOLO-based models have effectively improved the detection speed, but there is still room for improvement in the detection accuracy. [PERSON] et al. [21] proposed a novel strawberry detection algorithm based on a unique combination of YOLOv7 and augmented reality technology, which achieved a high F1 score of 92%. However, the method's accuracy in natural environments cannot be guaranteed, as the detection environment was limited to a greenhouse. [PERSON] et al. [22] proposed a multi-stage strawberry detection algorithm by integrating the AFF (adaptive spatial feature fusion) module into YOLOv5, which improved the detection performance in natural environments, with an average precision exceeding 90%. However, this integration also led to a significant decrease in detection speed. In summary, most research focused on improving specific performance metrics without achieving a well-balanced trade-off between model accuracy, speed, and size. To address this problem, an RTF-YOLO network model based on YOLOv5 is proposed in this paper to detect strawberry fruits. The main contributions of this work are summarized as follows:  
1. A novel neural network model was proposed for strawberry detection under varying illumination and occlusion scenarios, which demonstrated improved speed and accuracy compared with other mainstream networks. 2. An efficient convolution module based on structural reparameterization was proposed and fused into the backbone and neck networks, improving the model's detection speed from 122 to 145 FPS. 3. The triplet attention mechanism and focal loss function were introduced to improve the detection precision, which led to a 3.6% increase in the \(mAP_{0.5}\), reaching 90.24%. The rest of this article is organized as follows. Section 2 introduces the specific algorithm, while Section 3 focuses on the results of the conducted experiments to evaluate and compare the proposed model with state-of-the-art methods. Section 4 summarizes the main conclusions of this work.