# DP-YOLO: Effective Improvement Based on YOLO Detector
## 5 Conclusions  
The field of deeplearning-based object detection methods exhibits tremendous promise and also faces several challenges. In pursuit of further enhancing detection effectiveness and addressing the complex issue of large intra-class variations resulting from target deformations and attitude changes, we have made significant improvements to the YOLOv5 detection algorithm. These enhancements encompass label assignment optimization and structural modifications to the backbone network, leading to a refined and more accurate model. Crucially, these advancements have been achieved while maintaining a small computational cost. The resulting method, aptly named DP-YOLO, undergoes rigorous validation on the Urised11 dataset. The evaluation affirms its effectiveness in bolstering the detector's comprehension of target features, resulting in greater adaptability to various instances of target deformations and attitude changes. Moreover, this improved paradigm can be seamlessly integrated into other anchor-based detectors utilizing grid concepts, such as YOLOv3 and YOLOv7. Meanwhile, more issues are worth exploring, such as the stochastic nature of the PSA, which leads to the fact that there is still space for the improvement of DP-YOLO in small target detection.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Eryth** & **Leuko** & **Cryst** & **Epith** & **Yeast** & **Cast** & **Leukoc** & **Mycete** & **Sperm** & **Pcast** & **Epith** \\ \hline YOLOv3 & 73.7 & 87.3 & 82.5 & 84.8 & 85.9 & 64.4 & 65.6 & 45.9 & 57.1 & 56.6 & 44.7 \\ Faster RCNN & 75.3 & 90.6 & 87.6 & 91.3 & 88.5 & 72.6 & 76.4 & 43.5 & 35.6 & 62.7 & 55.1 \\ Retinanet & 74.9 & 91.1 & 87.3 & 92.2 & 89.8 & 73.5 & 77.2 & 41.1 & 37.2 & 63.1 & 54.7 \\ FCOS & 75.9 & 92.6 & 88.7 & 92.2 & 89.5 & 71.7 & 80.2 & 53.4 & 47.8 & 66.4 & 54.9 \\ YOLOv5 & 75.8 & 92.2 & 87.3 & 90.1 & 90.4 & 71.4 & 76.4 & 56.8 & 58.9 & 62.9 & 49.8 \\ SSD & 83 & 92.4 & 89.4 & 91 & 90.3 & 74.6 & 75.9 & 42.5 & 52.4 & 63 & 57.8 \\ YOLOX & 97.4 & 93.3 & 88.4 & 91.8 & 91.1 & 68.9 & 74.7 & 54 & 52 & 63 & 52.8 \\ DP – YOLO & 76.1 & 93.6 & 89.1 & 91.4 & 91.7 & 72.4 & 74.3 & 61.1 & 67.3 & 64.9 & 55.8 \\  & +0.3 & +1.4 & +1.8 & +1.3 & +1.3 & +1 & \(-\)1.9 & +4.3 & +8.4 & +2 & +6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with SOTA method under each category.  
Figure 7: Comparison of DP-YOLO and YOLOv5 on different datasets.  
**Author Contributions:** Conceptualization, Q.W.; methodology, C.W. and Y.Q.; writing--original draft preparation, C.W.; writing--review and editing, Q.W. and H.W.; data collection: C.W., Y.H. and Y.X.; analysis and interpretation of results: C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the national natural science foundation of China (No. 61773360, 61973295), the Anhui province's quality engineering project (No. 2015 jxtd044) and the Academic funding project for top talents of disciplines in Colleges and universities of Anhui Province (No. gxbjZD2020096).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.