# Fast Target Recognition Based on Improved ORB Feature
## 4 Experimental Results and Analysis  
Under natural conditions, illumination has a great influence on collected images and different illumination conditions may cause underexposure or overexposure to affect the quality of images, which brings inconvenience to subsequent matching research. This paper re-distributes the brightness of the image by adopting the adaptive histogram equalization, reducing the influence of the light on the input picture, and the advantages and disadvantages are measured by the number of feature points, the number of matching points, and the running time.  
To verify the feasibility of the ORB algorithm, this paper compares the traditional ORB algorithm with the improved ORB algorithm, and the experimental environment is Pycharm 2021 and OpenCV 4.5.2. The recognition target map is a common book in the laboratory, and this paper will verify the two groups of images. The first group of pictures is an overexposed image experimental scene with a size of 523 \(\times\) 481 in PNG format (Figures 6-10). The second group is an underexposed picture experiment scene with a size of 526 \(\times\) 489 in PNG format (Figures 11-15). The experimental results of the ORB algorithm and the ORB algorithm based on image enhancement are compared respectively.  
Figure 6: Raw images of the two targets under overexposure conditions.  
Figure 7: Feature points extracted by the traditional ORB algorithm under overexposure conditions.  
Figure 8: Feature matching image extracted by the traditional ORB algorithm under overexposure conditions.  
Figure 10: The improved ORB algorithm is used to extract feature matching images under overexposure conditions.  
Figure 9: Feature points extracted by the improved ORB algorithm under overexposure conditions.  
Figure 11: Raw images of the two targets under underexposed conditions.  
Figure 12: Feature points extracted by the traditional ORB algorithm under underexposed conditions.  
Figure 13: Feature matching image extracted by the traditional ORB algorithm under underexposed conditions.  
A comparison of the data of Tables 1 and 2 shows that the improved ORB algorithm not only maintains the superiority of the ORB itself but also improves the matching effect under conditions of insufficient exposure or excessive exposure, where under excessive exposure conditions, the characteristic points increased by 78% and the matching point increased by 45%, and under the conditions of insufficient exposure, the characteristic points increased by 23% and the matching point increased by 34%. The effectiveness of the algorithm was explained by the above experiments and comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Feature Points of the** & **Feature Points of the** & **Matching Points** & **Running Time** \\  & **Left Graph** & **Right Graph** & **(ms)** \\ \hline Traditional ORB algorithm & 3067 & 3112 & 734 & 304 \\ \hline SIFT algorithm & 722 & 650 & 297 & 109 \\ \hline Improved ORB algorithm & 5468 & 5519 & 1066 & 372 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matching data of overexposed images.  
Figure 14: Feature points extracted by the improved ORB algorithm under underexposure conditions.  
Figure 15: The improved ORB algorithm is used to extract feature matching images under underexposure conditions.