# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 3 Experiments And Results
### Ablation Experiments of Different Improved Models
Comparative Experimental Analysis of Different Models  
We compared RTF-YOLO with other mainstream target detection networks, including YOLOv5s, YOLOv5n, ASFF-YOLOv5 [22], DSE-YOLOv5 [19], DSW-YOLOv5 [8], and Faster R-CNN, as shown in Table 7. According to the experimental results, Faster R-CNN achieved a recall rate of 81.04%, but its precision rate was only 38.43%, which led to many false detections of strawberries. In contrast, SSD achieved a precision rate of 82.33%, but its recall rate was only 19.24%, which led to many missed detections. Faster RCNN employed a single high-level feature map for target classification and localization, which led to its inadequate detection ability for small- and multi-scale targets. Although SSD used multi-scale feature maps from different layers, it failed to fully exploit the information from low-level high-resolution feature maps, which is vital for small object detection. As a result, neither of the two networks could meet the requirements for multi-stage strawberry detection. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & _P (\%)_ & _R (\%)_ & _mAP\({}_{0.5}\) (\%)_ & **Params (M)** & **FPS** \\ \hline Faster R-CNN & 38.43 & 81.04 & 64.32 & 137.10 & 23 \\ SSD & 82.33 & 19.24 & 63.24 & 26.29 & 50 \\ YOLOv5 & 89.67 & 77.21 & 86.65 & **7.03** & 122 \\ ASFF-YOLOv5 & 86.00 & 81.75 & 87.25 & 12.46 & 100 \\ DSE-YOLO & 85.34 & 81.09 & 87.98 & 224.39 & 21 \\ DSW-YOLO & 82.80 & **82.10** & 86.70 & 32.40 & 42 \\ RTF-YOLO & **91.92** & 81.43 & **90.24** & 13.00 & **145** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of performance and efficiency metrics for various object detection models. Figure 10: Visualization results of YOLOv5, R-YOLO, RT-YOLO, and RTF-YOLO are shown in their respective columns, with subfigures (**a**–**c**) serving as three examples. The false positive samples are encircled with a yellow dashed line, while the false negative samples are encircled with a blue dashed line. YOLOv5 utilized the PANet network to integrate feature maps from diverse levels, which enhanced the receptive field and achieved a richer input representation. Models based on YOLOv5 achieved better detection accuracy. The \(mAP_{0.5}\) metric for RTF-YOLO was 90.24%, which showed improvements of 3.6% and 3% compared with YOLOv5s and ASFF-YOLOv5, respectively. YOLOv5's single-stage detection scheme offered a higher detection speed. However, introducing enhancement modules, such as the ASFF module, DSE module, and attention mechanism, increased the model complexity and reduced the detection speed. Benefiting from RTF-YOLO's improved backbone network, its detection speed surpassed YOLOv5s and other YOLO-based improved networks by over 19% and 45%, respectively. Although DSE-YOLO and DSW-YOLO achieved a high detection accuracy, the large model sizes and slower detection speeds limit their deployment on edge computing devices. In contrast, RTF-YOLO offered a threefold advantage in detection speed and model scale. Therefore, RTF-YOLO was more suitable for detecting strawberries in complex environments in real time.