# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 4 Analysis of Results
### Comparative Experiments of Different Algorithms  
In order to assess the impact of algorithm enhancements, this paper conducted experimental verification on the self-made dataset PSO 2023 using the object detection algorithms YOLOv4, YOLOv4-tiny, Faster-RCNN, YOLOv5, YOLOv7, and YOLOv8. The experimental results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **mAP\_0.5/\%** & **FPS/f\_s\({}^{-1}\)** & **Volume/MB** \\ \hline YOLOv4 & CSPDarknet53 & 91.6 & 36.9 & 244.5 \\ YOLOv4-tiny & CSPDarknet53-Tiny & 83.9 & 153.9 & 22.5 \\ \hline Faster-RCNN & Resnet50 & 82.3 & 23.0 & 108.0 \\ Faster-RCNN & VGG16 & 88.5 & 17.6 & 521.0 \\ \hline YOLOv5s & CSPDarknet53 & 94.3 & 303.0 & 14.4 \\ YOLOv5m & CSPDarknet53 & 94.6 & 263.2 & 40.3 \\ YOLOv51 & CSPDarknet53 & 95.3 & 156.3 & 88.6 \\ YOLOv5x & CSPDarknet53 & 95.7 & 94.3 & 165.2 \\ \hline YOLOv7 & CSPDarknet53 & 95.0 & 104.2 & 71.5 \\ \hline YOLOv8s & Darknet53 & 97.5 & 109.9 & 22.5 \\ \hline MFAMAM-YOLOv5s & CSPDarknet53 & 97.4 & 400.0 & 19.0 \\ MFAMAM-YOLOv5m & CSPDarknet53 & 97.8 & 277.8 & 61.3 \\ (our) & CSPDarknet53 & 98.2 & 147.1 & 97.1 \\ MFAMAM-YOLOv5x & CSPDarknet53 & 98.8 & 88.5 & 179.7 \\ (our) & & CSPDarknet53 & 98.8 & 179.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative Results of Experiments with Different Models.  
It is evident from the table that the two-stage object detection algorithm, Faster-RCNN, did not achieve higher accuracy despite using the Resnet50 backbone network. Instead, it resulted in a slower detection frame rate. YOLOv4-tiny and YOLOv5, commonly used lightweight algorithms, may have certain advantages in model deployment. However, they are not sufficiently outstanding in terms of detection accuracy and real-time performance for pole-shaped obstacles in complex environments. As a result, they struggle to meet the requirements of assisted driving perception technology. Although the YOLOv7 and YOLOv8 algorithms have a relatively good detection accuracy, the YOLOv7 model is larger and is not the optimal choice for deploying a perception system. Although the YOLOv8s model has a high detection accuracy, the image detection rate is not high. For an object detection system, it is necessary to ensure both accurate and speedy detection. Therefore, the method proposed in this article improves the mean Average Precision (mAP) to varying degrees in MFMAM-YOLOv5s, MFMAM-YOLOv5m, MFMAM-YOLOv5l, and MFMAM-YOLOv5x. In particular, MFMAM-YOLOv5s has significantly improved frames per second (FPS) and mean average precision (mAP) values compared to YOLOv5s and YOLOv7. Compared to the aforementioned models, the MFMAM-YOLOv5s model has a better balance between speed and accuracy, making it more suitable for deployment in assisted driving systems.  
To visually evaluate the performance of the improved algorithm in terms of detection accuracy and robustness, this article selected the test result images of the YOLOv5s, YOLOv7, YOLOv8s, and MFMAM-YOLOv5s algorithms on the test dataset. These images are shown in Figure 9.  
From the comparative effect chart shown in Figure 9, it can be observed that the YOLOv5s network model exhibits a relatively high false detection rate and missed detection rate in environments with obstructions, rainy and snowy weather, and low visibility, although it is capable of detecting pole-like obstacles. The YOLOv7 and YOLOv8s network models also experience higher false detection and missed detection rates in complex environments with partial feature obstructions, as well as in rainy and snowy weather. However, compared to the aforementioned object detection algorithms, the improved  
Figure 9: Comparison Results Between Different Algorithms. (**a**) YOLOv5s; (**b**) YOLOv7; (**c**) YOLOv8s; (**d**) MFMAM-YOLOv5s.  
MFMAM-YOLOv5s network model is still capable of accurately detecting pole-like obstacles in complex environments.