## 5 Results
### Feature Matching Experiment
#### 5.1.2 Experimental Environment Configuration and Parameter Setting  
The computer hardware specifications used in this experiment are as follows: AMD Ryzen 7 5800H processor (USA), 16 GB of memory (USA), a 64-bit Windows 11 operating system, and Visual Studio 2017 as the programming software. The third-party library OpenCV 4.1.0 was used for ORB feature extraction and description.  
To validate the advantage of the algorithm proposed in this paper (ORB+PEmatch), the matching performance is compared with the traditional ORB algorithm, ORB+HAMMING algorithm [31], and ORB+RANSAC algorithm [32] in the actual environment of a photovoltaic power plant. The traditional ORB algorithm provides the same set of coarse matching point pairs as for the other three algorithms. The ORB+HAMMING algorithm uses twice the minimum HAMMING distance as the threshold to eliminate mismatches. ORB+RANSAC employs the random sample consensus algorithm to compute the fundamental matrix, with an inlier distance threshold set to three. After obtaining the fundamental matrix, mismatches are eliminated using the epipolar constraint with a common threshold of three. The inlier distance threshold is the pixel distance from the matching points to the epipolar line. Due to the noise often present in images, setting the inlier distance threshold too small can filter out correct matches, while setting it too large may result in missing incorrect matches. Based on multiple experiments and considering commonly used values from past research, the experiment adopts an approximated optimal value of three as the inlier distance threshold.  
Random sampling is contrasted with the particle swarm optimization sampling discussed herein. To ensure fairness in performance comparison, the PE-match's inner point distance threshold is set to three. Based on multiple experimental results, the near-optimal parameter settings for the particle swarm are determined: the population size is set to 20, which influences the search capability of the particle swarm algorithm, though an excessively high number can reduce the algorithm's real-time performance. The number of differences \(n\) is set to three, influencing the mutation strategy's sensitivity to differences among particles. The crossover probability \(L\) is set to 50%, determining the influence of the mutation strategy on \(Pbest\). The learning weights for individual and global extrema \(c_{1}\) and \(c_{2}\) are both set to 0.5, while the inertia weight \(\omega\) is also set to 0.5. The number of matching point pairs \(j\) involved in fitness calculation is set to 100; this value allows the particle swarm algorithm to maintain real-time performance without degrading despite an increase in feature points, though setting it too low can diminish the optimization capability. The iteration ends, and the optimal fundamental matrix is output if there are no significant changes in \(Gbest\) over 8 (\(1t\_\)max) iterations, a measure included to ensure the stability of the algorithm's real-time performance. Taking the 1-4 matching experiments of Scene 1 from the OxfordVGG dataset as an example, Figure 9 illustrates the influence of key parameters on the algorithm's accuracy and processing time. For different matching scenarios, this paper will make minor adjustments based on the aforementioned parameters to adapt to varying conditions. Because the outcomes of the particle swarm optimization are related to the number of iterations and the initial distribution of particles, subsequent experimental data related to this algorithm will be the averages of multiple trials.  
Figure 8: Optical images of OxfordVGG dataset.