# DP-YOLO: Effective Improvement Based on YOLO Detector
## 3 Approaches
### Urine Sediment Dataset
#### 3.3.1 Urine Sediment Detection  
Urine sediment detection plays a crucial role in clinical pathology, particularly in evaluating renal, urologic, metabolic, and diabetic disorders. It involves the microscopic examination of a urine sample to analyze the density and number of cells, crystals, and other particles present. The interpretation of urine sediment composition provides valuable information about underlying diseases. For instance, the presence of red blood cells or a tubular pattern may indicate glomerular disease, while the presence of white blood cells may indicate infection or inflammation, and the presence of crystals may suggest metabolic disorders, among other possibilities. Traditionally, this examination process has been manually conducted by experienced professionals. However, this approach is time-consuming, labor-intensive, subject to inaccuracies, and prone to subjective human errors. In previous years, researchers have explored the use of traditional machine learning algorithms in automating this process, and despite these efforts, achieving accurate and efficient results suitable for practical clinical use remains a challenge.  
Indeed, the advent of deep-learning-based target detection techniques has opened up promising possibilities for automating the urine sediment detection process. Researchers have made notable strides in this area, demonstrating the potential of deep learning in this specialized domain. For instance, [PERSON] et al. [30] introduced a multi-view residual model that achieved significant advancements in automated urine sediment detection on their self-constructed USE dataset. This work showcased the power of deep learning in efficiently analyzing urine sediment compositions and extracting valuable information from microscopic images. Additionally, [PERSON] et al. [31] applied innovative super-resolution and denoising image processing techniques to further enhance urinary sediment detection. Their approach demonstrated how combining deep learning with sophisticated image processing methods can lead to improved accuracy and reliability in detecting and analyzing urinary sediment samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Stage 1**} & \multicolumn{2}{c}{**Stage 2**} & \multicolumn{2}{c}{**Stage 3**} & \multicolumn{2}{c}{**Stage 4**} \\ \cline{2-9}  & **Params** & **GFLOPs** & **Params** & **GFLOPs** & **Params** & **GFLOPs** & **Params** & **GFLOPs** \\ \hline C3 & 18.8 K & 31.5 & 115.7 K & 47.8 & 625.2 K & 64.3 & 1.4 M & 21.6 \\ D2C3 & 50.1 K & 67.3 & 240.5 K & 68.6 & 999.1 K & 57.2 & 1.2 M & 30.4 \\ D3C3 & 16.7 K & 16.7 & 96.6 K & 39.9 & 503.2 K & 51.7 & 1.0 M & 26.1 \\ \hline \end{tabular}
\end{table}
Table 1: Cost of different downsampling phases for three blocks in YOLO v5s backbone network.  
Figure 4: Network structure of YOLOv5s. Both simplify and keep in the figure indicate convolution operations using \(1\times 1\) convolution kernels, with the difference that the former halves the number of channels in the processing tensor, while the latter remains unchanged.  
Figure 3: Composition of the three bottleneck structures. Depending on the version of deformable convolution operator, there are two types of modules, D2C3 and D3C3.