# A Benchmark for the Evaluation of Corner Detectors
## 3 Benchmark Evaluation Analysis
### Objective Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detector** & **Dataset 1** & **Dataset 2** & **Dataset 3** \\  & [4,38] & [38–40] & **(Urban-Corner)** \\ \hline SUSAN [2] & 0.1683/0.0650 & 0.2363/0.7387 & 0.0501/**0.8943** \\  & 0.1167/0.0540 & 0.4875/0.2493 & 0.4720/0.0910 \\ \hline Harris-Laplacep [3] & 0.1530/0.4389 & 0.3912/0.5229 & 0.9594/0.3333 \\  & 0.2959/0.2203 & 0.4570/0.4303 & 0.3553/0.2814 \\ \hline MSCP [19] & 0.8224/0.9517 & 0.8271/0.7450 & 0.7256/0.5918 \\  & 0.8870/0.8661 & 0.7860/0.7692 & 0.6587/0.6098 \\ \hline CPDA [22] & 0.9368/0.8198 & 0.9355/0.6028 & 0.8154/0.6143 \\  & 0.8783/0.8595 & 0.7691/0.7203 & 0.7186/0.7008 \\ \hline He \& Yung [21] & 0.9132/0.9170 & 0.8165/**0.8302** & 0.4408/0.7392 \\  & 0.9151/0.9104 & 0.8234/0.8132 & 0.5900/0.5173 \\ \hline Fast-CPDA [37] & 0.9350/0.8006 & 0.9403/0.6095 & 0.8198/0.6183 \\  & 0.8678/0.8440 & 0.7749/0.7292 & 0.7187/0.7042 \\ \hline DOG [35] & 0.8493/0.8687 & 0.8665/0.6462 & 0.7356/0.5809 \\  & 0.8590/0.8557 & 0.7563/0.7279 & 0.6583/0.6033 \\ \hline GCM [23] & 0.8744/0.9472 & 0.8405/0.7246 & 0.7115/0.6091 \\  & 0.9108/0.8974 & 0.7825/0.7633 & 0.6603/0.6189 \\ \hline ANDD [25] & 0.8181/0.9436 & 0.6568/0.7304 & 0.3963/0.5927 \\  & 0.8809/0.8667 & 0.6936/0.6753 & 0.4945/0.4020 \\ \hline WEAE [26] & 0.8703/0.9694 & 0.8566/0.7910 & 0.7234/0.6921 \\  & 0.9199/0.9049 & 0.8238/0.8114 & 0.7078/0.6516 \\ \hline ASJ [5] & 0.5849/0.7289 & 0.4207/0.8132 & 0.6646/0.6411 \\  & 0.6569/0.6206 & 0.6169/0.5392 & 0.6528/0.6123 \\ \hline SuperPoint [6] & 0.4622/0.7142 & 0.6526/0.5191 & 0.4808/0.4086 \\  & 0.5882/0.5282 & 0.5858/0.5704 & 0.4447/0.3945 \\ \hline New-Curvature [27] & 0.8409/0.9694 & 0.8806/0.7451 & 0.8218/0.6614 \\  & 0.9052/0.8872 & 0.8128/0.7987 & 0.7416/0.6784 \\ \hline SOGGDD [7] & 0.4267/**0.9895** & 0.8403/0.7416 & 0.5260/0.6305 \\  & 0.7081/0.5625 & 0.7910/0.7776 & 0.5782/0.5159 \\ \hline MSRJ [28] & 0.9426/0.9006 & **0.9372**/0.7045 & 0.7428/0.6051 \\  & 0.9216/0.9146 & 0.8208/0.7922 & 0.6740/0.6300 \\ \hline Zhang \& Sun [8] & 0.3196/0.9725 & 0.8221/0.7161 & 0.6941/0.5817 \\  & 0.6461/0.4385 & 0.7691/0.7562 & 0.6379/0.5833 \\ \hline RPD [30] & 0.6762/0.9778 & 0.7726/0.7866 & 0.6978/0.6970 \\  & 0.8270/0.7760 & 0.7796/0.7703 & 0.6974/0.6442 \\ \hline DMC [33] & **0.9587**/0.9381 & 0.8723/0.7904 & **0.8195**/0.7102 \\  & **0.9484/0.9447** & **0.8313**/**0.8176** & **0.7649**/**0.7119** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation results in terms of precision/recall (first entry) and APR/F score (second entry). The best performance with respect to each used metric achieved on each image dataset is highlighted in boldface. In more detail, the performance of the evaluated corner detectors for the attacked images is depicted in Figure 4. It can be seen from the first row that these 18 detectors were all influenced by the rotated images. Most detectors performed well at rotation angles of \(-\pi/2\) and \(\pi/2\) and received a certain degree of volatility at other angles. The second row of Figure 4 shows that the evaluated corner detectors had different fluctuations in the case of uniform scaling. New-Curvature [27] performed better than the other detectors in this case. From the last two sub-figures in the third row, it can be seen that each detector performed stably on Dataset 2 and Dataset 3 when the JPEG quality factor was greater than