# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 4 Analysis of Results
### Binocular Camera Target Ranging  
The detection of pole-shaped obstacles not only requires more accurate identification and classification but also requires a sufficient ranging accuracy within a certain range. Therefore, this paper first uses the improved network model to obtain the coordinate information of the target; then, the coordinate information of the target and the stereo matching in the binocular ranging algorithm are correlated to obtain a more accurate measurement distance through the calculation of multiple information. To further validate the accuracy and robustness of this method, real road test experiments were conducted. First, a section of an urban road is arbitrarily selected to detect road cones using the method in a complex environment; then, the center of light of the vehicle-mounted camera is taken as the origin, and a laser rangefinder is used to verify and read the real distance. At the same time, the error of the laser rangefinder is \(\pm\)1.5 mm, which is in line with the measurement accuracy. Finally, the above experiments were repeated and recorded by constantly changing the distance. At the same time, to better demonstrate the error between the actual distance and the test distance, this paper compares and analyzes the actual distance, the test distance, the absolute error, and the relative error of the 80 groups of experimental results and draws a comparative statistical graph, as shown in Figure 10.  
From Figure 10a, it can be observed that the output distance of the pole-shaped obstacle detection system fluctuates above and below the true distance. Meanwhile, as the distance increases, the fluctuation also increases. As for Figure 10b, it shows the absolute error, which clearly increases with distance. In other words, the absolute error value is proportional to the distance. From Figure 10c, it can be visually seen that the relative error within a range of 6 m is less than 5%. The relative error within a range of 3-15 m is less than 7%, which meets the accuracy requirements for distance measurement. In conclusion, as the distance increases, the measurement accuracy of the pole-shaped obstacles gradually decreases, but the overall detection accuracy fully meets the requirements. The above experiments were all conducted on static straight roads. However, if the vehicle is traveling in a dynamic scene, it will have a certain impact on the de-detection accuracy and real-time performance. As the vehicle is moving rapidly, the feature information of the pole obstacle is also changing, such as the length, size, angle, etc. The limitation of binocular camera hardware and the vibration of the vehicle body will cause some difficulties in the feature extraction of rod-shaped obstacles. Therefore, to further validate the accuracy and robustness of the improved algorithm in dynamic situations, a  
Figure 10: Statistical Comparison Chart of Ranging Experiment Results. (**a**) Comparison of actual distance and test distance; (**b**) Absolute error statistics chart; (**c**) Relative error statistics. section of the curve was directly selected for testing in this experiment. If the algorithm can accurately detect pole-shaped obstacles while the vehicle is making a turn, it would serve as a good validation for road detection in dynamic scenarios. According to the regulations of the Road Traffic Safety Law [37], the maximum speed for vehicles to make a turn is 30 km/h. Therefore, this study conducted experiments using the maximum turning speed. In this paper, 10 turning tests were conducted with the maximum turning speed of 30 km/h throughout the experiment, and 20 sets of experimental data were obtained, as shown in Table 8.  
From the test experimental results in Table 8, it can be observed that the detection accuracy of the objects is somewhat affected due to the rapid changes in target features during the turning process.