# DP-YOLO: Effective Improvement Based on YOLO Detector
## 4 Experiment
### Comparison with SOTA Method on Urised11 Dataset  
In the pursuit of fair comparisons, we select backbone networks with sizes that closely align with the same order of magnitude for different detection frameworks. For instance, Faster RCNN [3], Retinanet [6], and FCOS [11] employ ResNet18 [7] as their backbone. On the other hand, SSD [5] and YOLOv3 [19] rely on VGG16 [38] and Darkne53, respectively. Finally, YOLOv5 and YOLO X [21] opt for model size classes, all denoted as \"\(s\)\". Table 7 showcases the results of evaluating the COCO 2017 dataset, organized in ascending order of Average Precision (AP). Our method demonstrates unparalleled detection accuracy, outperforming numerous state-of-the-art methods, all while utilizing a relatively modest number of parameters and computational resources. Regarding the detection results for each category in Urised11, Table 8 provides the AP detected for each category at an IoU threshold of 0.5 for NMS (non-maximum suppression). Remarkably, our method showcases significant improvements in detection accuracy relative to YOLOv5 across all seven categories of targets with large intra-class differences in Table 3. Of particular note is the remarkable improvement in the mycete, sperm, pcast, and epithn categories. In comparison to other SOTA (state-of-the-art) methods, FCOS [11], SSD [5] and YOLOX [21] achieve the highest AP\({}_{50}\) in some categories. However, our method outperforms all competitors, securing the best overall performance. It was either ranked first or at the top of the list in the seven categories shown in Table 3. This accomplishment \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**I + J** & **\#Params** & **GFLOPs** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{s}}\)** & **AP\({}_{\mathbf{m}}\)** & **AP\({}_{\mathbf{I}}\)** \\ \hline - & 7.2 \(M\) & 16.4 & 38.0 & 58.0 & 40.8 & 22.7 & 43.0 & 48.8 \\ \(0+4\) & **6.9 \(M\)** & **15.6** & 40.2 & 60.0 & 43.4 & 23.7 & 44.8 & 53.1 \\ \(1+3\) & 6.9 \(M\) & 16.8 & 40.6 & 60.5 & 43.8 & 24.0 & 45.3 & 54.0 \\ \(2+2\) & 7.1 \(M\) & 17.7 & 40.9 & 60.7 & 44.0 & 24.8 & 45.7 & **54.3** \\ \(3+1\) (*) & 7.6 \(M\) & 17.9 & **41.2** & **61.3** & **44.4** & **24.8** & **46.0** & 54.0 \\ \(4+0\) & 8.0 \(M\) & 17.7 & 40.6 & 60.4 & 44.0 & 24.4 & 45.4 & 54.2 \\ \hline \end{tabular}
* indicates that the result is best and the numbers in bold indicate the optimal value.  
\end{table} 
Table 6: Exploration of D2C3 and D3C3 replacement backbone networks. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Method** & **Size** & **\#Params** & **GFLOPs** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{s}}\)** & **AP\({}_{\mathbf{m}}\)** & **AP\({}_{\mathbf{I}}\)** \\ \hline YOLOv3 & [608; 608] & 61.5\(M\) & 58.9 & 43.0 & 68.1 & 50.4 & 35.5 & 45.4 & 45.3 \\ Faster RCNN & [1333; 800] & 28.3\(M\) & 126 & 45.6 & 70.8 & 53.2 & 35.3 & 48.6 & 48.3 \\ Retinanet & [1333; 800] & 20.0\(M\) & 126 & 47.1 & 71.1 & 55.2 & 37.8 & 49.1 & 53.2 \\ FCOS & [1024; 1024] & 19.2\(M\) & 122 & 47.2 & 73.9 & 54.1 & 31.1 & 50.2 & 49.7 \\ YOLOv5 & [640; 640] & 7.0\(M\) & 15.8 & 48.2 & 73.8 & 56.5 & 33.6 & 50.3 & 52.8 \\ SSD & [512; 512] & 25.9\(M\) & 88.9 & 48.4 & 73.8 & 57.5 & 39.1 & 51.1 & 52.1 \\ YOLOX & [640; 640] & 8.9\(M\) & 13.3 & 48.7 & 75.2 & 58.4 & 37.8 & 51.6 & 49.8 \\ DP – YOLO & [640; 640] & 7.4\(M\) & 17.3 & 49.2 & 76.2 & 57.2 & 32.8 & 50.7 & 54.1 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison with SOTA method on Urised11 dataset. exemplifies its robustness in detecting categories with significant intra-class variance which is the main reason for the overall improvement in detection performance. Figure 7 represents the progress of our approach during the training process, and we can see that there are some fluctuations in the training of DP-YOLO on the Urised11 dataset as the number of training epochs increases. However, this problem is smaller relative to YOLOv5, which may indicate that the size of this dataset needs to be expanded.