# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 3 Experiments
### Comparison with State-of-the-Art Oriented Object Detection Methods  
In this section, we compare YOLO-OHFD with state-of-the-art rotation box detection networks. We conducted experiments using the same PC to quantify the mAP and PFS of each algorithm in the oriented hair follicle detection task. First, it can be seen through Table 6 that most of the detection algorithms are relatively effective in detecting follicles with fewer hairs (Class1, Class2), while their detection accuracy for follicles with more hairs (Class3, Class4) is relatively low. This result is mainly due to the irregular distribution of hairs in multi-hair follicles, and the hairs may obscure each other under different dermatoscopic shooting angles. Therefore, the multi-hair follicles show the characteristics of the hairless follicles. Then, in terms of the detection accuracy of the algorithm, our method achieved a mAP of 87.01 on the hair follicle detection dataset, and the detection accuracy is 5.92% higher than that of ReDet. Compared to the methods we propose, most of the existing rotation detection methods are mainly used for aerial image detection or text detection in natural scene images. They focus on the classification as well as the localization of different kinds of objects in the overall background. However, in the task of hair follicle recognition, it is \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Class1** & **Class2** & **Class3** & **Class4** & **mAp** & **FPS** & **FLOPS (G)** & **Paras (M)** & **MIT (ms)** \\ \hline R\({}^{2}\)CNN [20] & 55.68 & 54.01 & 52.57 & 50.59 & 54.17 & 9.63 & 163.86 & 60.14 & 103.84 \\ RRPN [21] & 57.02 & 57.04 & 54.55 & 52.96 & 56.46 & 10.01 & 150.25 & 41.22 & 99.90 \\ SCRDet [22] & 62.95 & 62.84 & 60.15 & 58.2 & 62.24 & 16.37 & 74.20 & 39.30 & 61.09 \\ RoI-Trans [23] & 71.92 & 71.14 & 69.96 & 67.53 & 71.05 & 22.65 & 105.17 & 55.13 & 44.15 \\ G.Vertex [24] & 74.58 & 73.94 & 71.26 & 69.44 & 73.52 & 29.66 & 105.43 & 41.13 & 33.72 \\ DRN [25] & 76.22 & 75.61 & 72.8 & 71.34 & 75.17 & 13.58 & 189.81 & 20.65 & 73.64 \\ R\({}^{3}\)Det [26] & 78.97 & 79 & 77.03 & 74.68 & 78.49 & 12.86 & 132.63 & 41.90 & 77.76 \\ CenterMap [27] & 79.79 & 79.4 & 77 & 74.43 & 78.93 & 33.57 & 130 & 41.11 & 29.79 \\ S\({}^{2}\)A-Net [28] & 81.62 & 81.74 & 79.84 & 77.8 & 81.23 & 37.51 & 101.51 & 38.62 & 26.66 \\ ReDet [29] & 83.4 & 82.1 & 80.76 & 78.33 & 82.15 & 14.19 & 48.39 & 31.62 & 70.47 \\ YOLO-OHFD & 87.6 & 87.5 & 85.37 & 83.25 & 87.01 & 43.67 & 42.57 & 22.57 & 22.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our method with state-of-the-art oriented object detection methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Class1** & **Class2** & **Class3** & **Class4** & **mAp** & **FPS** \\ \hline IoU & 79.41 & 81.09 & 79.53 & 78.26 & 80.18 & 44.97 \\ GIoU & 81.07 & 82.13 & 80.03 & 78.49 & 81.30 & 44.22 \\ DIoU & 81.19 & 82.31 & 80.02 & 78.63 & 81.43 & 44.13 \\ CIoU & 85.68 & 85.36 & 82.23 & 80.13 & 84.73 & 43.93 \\ EIoU & 88.47 & 87.34 & 84.35 & 81.83 & 86.99 & 43.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of different IoUs on detection accuracy. more difficult to correctly classify follicles according to the number of hairs in the follicle while considering the accuracy of follicle localization. It is difficult for general models to obtain better detection results. Our method fully considers the diversity and complexity of hair follicle information in the hair recognition, thus achieving better detection results. In terms of detection efficiency, our method achieved an FPS of 43.67, which is 16.42% higher than that of S2A-Net, demonstrating superior real-time processing capabilities. Additionally, our model is relatively simple, with fewer parameters and FLOPs compared to other methods. This indicates that our model has lower memory requirements and higher computational efficiency, making it easier to deploy across various hardware platforms.