# Dealing with Low Quality Images in Railway Obstacle Detection System
## 5 Comparative Analysis of Proposed and Existing Methods
### Results and Discussion
This operator proved to be very effective in the edge-detection problems because it uses non-maxima suppression and hysteresis thresholding in order to detect weaker and stronger edges. The obtained results are presented in Table 1 (_Canny method_ column). Finally, we also \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics/Methods** & **Proposed Method** & **FFT Method** & **Laplacian Method** & **Canny Method** \\ \hline TB & 233 & 219 & 200 & 222 \\ \hline FB & 55 & 104 & 92 & 64 \\ \hline TNB & 565 & 516 & 528 & 556 \\ \hline FNB & 19 & 33 & 52 & 30 \\ \hline Accuracy (Acc) & 0.915 & 0.843 & 0.835 & 0.892 \\ \hline Precision (Pre) & 0.809 & 0.678 & 0.685 & 0.776 \\ \hline Recall (Rec) & 0.925 & 0.869 & 0.794 & 0.881 \\ \hline F-score (F) & 0.863 & 0.762 & 0.735 & 0.825 \\ \hline Computational time & 241 s & 305 s & 203 s & 356 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of different blur-detection approaches. tested the edge-detection methods based on the first-order derivative operators--[PERSON], [PERSON] and [PERSON] [37]. The acquired findings revealed that these approaches produced excellent results in time-consuming issues since they are relatively simple, but their overall performance was remarkably low when compared to the proposed algorithm. In order not to further complicate the graphs presented below, we did not present these data because they seem incomparable for our specific railway application. According to the table above and the bar graph presented in Figure 7, the proposed method produces the best overall results in the railway application for each of the proposed evaluation measures. It meets the predefined requirements for simplicity and sufficient accuracy, which is critical when building a database of undistorted images to be used to train deep learning models for the processing and rejection of images from the processing pipeline. With regard to the time-consuming issue, the proposed algorithm is slightly slower than the Laplacian method, but considerably faster than the FFT and Canny methods. This can be explained by the fact that finding a threshold takes a little longer by default, but the proposed technique is significantly superior in the long run. It should be emphasized that, rather than making a binary judgment, all of the presented approaches above produce a score in a specific range to reflect the extent of blur that is observed. In order to calculate the evaluation measures, we first normalized the threshold values between 0 and 1, and after that, we transformed the scores into binary blur/non-blur decisions by searching for the most appropriate normalized threshold value for each method separately. Figure 8 shows the results of the performance evaluation on the railway dataset between the different approaches in the form of a precision-recall curve. Within practically the entire recall range [0, 1], the proposed improved algorithm yields the highest precision. The main reason for our method's success can be attributed to the designed improvements discussed in the previous section. Finally, all of the precision values for the proposed technique are greater than 0.5, indicating that there is a slight probability of missing real positive samples in all thresholds. Figure 7: Quantitative comparison of different blur detection approaches.