## 4 Experiments and Results
### Results of Image Quality Enhancement
#### 4.2.1 Measurement Method  
* Measurement of SR performance In the blind/referenceless image spatial quality evaluator (BRISQUE) [23], when mean subtraction and contrast normalization (MSCN) is applied, the histogram of pixels follows a Gaussian distribution, the generalized Gaussian distribution is mapped to the image histograms that undergo MSCN processing, and the shape information is used as features to assess the image quality. The structural similarity index (SSIM) [24] is a representative full-reference image-quality assessment method designed to evaluate the difference and similarity between the image quality of human visual perception, and not the numerical errors. For comparison of an original image A and a distorted image B, the SSIM compares the luminance, contrast, and structure of the two images. A value close to \(-1\) indicates high image quality, and a value close to 0 indicates low image quality. * Evaluation of model accuracy To evaluate the model accuracy, we employed general accuracy and average precision (AP), which is a representative measure of the prediction accuracy of DL detection models. By measuring the intersection over union between the ground truth object area and the area predicted by the model, we determined the classification performance according to the ratio of agreement between the prediction and the ground truth. AP 50 indicates that the classification was considered successful when the ground truth and prediction concurred by 50% or more. For AP 75, the ratio of agreement should be 75% or more. The notations APs/APm/API represent the value of AP measurements for s (small)/m (medium)/1 (large) objects, respectively, based on the size of the object to be detected, and success in the classification of classes is determined for all results with 50% to 95% agreement between the ground truth area and the predicted area. mAP represents the average performance of the model based on the mean of all AP values. 2.2 Measurement of SR Performance and Experiments and Evaluation for Improved Detection Model Performance  
In the first experiment, the SR performance of each model was tested and verified using image sets typically used in SR research. Figure 7 illustrates the five images used in this study. The resolution of each image was 640 \(\times\) 480, which was the result of upscaling the resolution by four times through SR and enhancing the image quality to 2560 \(\times\) 1920. \begin{table}
\begin{tabular}{c c c} \hline
**SRCNN** & **Blendmask** & **Mask-RCNN** \\ \hline \multicolumn{3}{c}{MODEL: BACKGROUND} \\ \multicolumn{3}{c}{BACKBONE:} \\ \multicolumn{3}{c}{NAME: “build\_fcos\_resnet\_fpn\_backbone”} \\ \multicolumn{3}{c}{BACKBONE:} \\ \multicolumn{3}{c}{NAME: “build\_fcos\_resnet\_fpn\_backbone”} \\ \multicolumn{3}{c}{DEPTH: 101} \\ \multicolumn{3}{c}{ROL\_HEADS:} \\ \multicolumn{3}{c}{epoch: 300} \\ \multicolumn{3}{c}{batch size: 100} \\ \multicolumn{3}{c}{loss rate: 1 \(\times\) 10\({}^{-4}\)} \\ \multicolumn{3}{c}{ \begin{tabular}{c} loss rate: 1 \(\times\) 10\({}^{-4}\) \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} loss rate: 1 \(\times\) 10\({}^{-4}\) \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} BAS rate: 0.01 \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} BIAS\_LR FACTOR: 1.0 \\ \end{tabular}  } \\ \multicolumn{3}{c}{MOMENTUM: 0.9} \\ \multicolumn{3}{c}{WARMUP\_METHOD: linear} \\ \multicolumn{3}{c}{WEIGHT\_DECAY: 0.0001} \\ \multicolumn{3}{c}{ \begin{tabular}{c} WEIGHT\_DECAY: 0.0001 \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} SOLVER: \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} INU\_FC: \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} BOLE\_REOLUTION: 7 \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} BOLE\_REOLUTION: 4 \\ \end{tabular}  } \\ \multicolumn{3}{c}{ \begin{tabular}{c} BOLE\_REOLUTION: 1 \\ \end{tabular}  } \\ \multicolumn{3}{c}{\ \begin{tabular}{c} BOLE\_REOLUTION: 1 \\ \end{tabular}