## 4 New Evaluation of Object Classification by the Target Response Approach  
The classifiers are used to classify/identify apples from an image. Two image parameters are considered as the varying parameters: contrast and brightness (Figure 7).  
The target response approach is adapted and implemented as a new performance assessment for classification approaches. In this section, the MobileNet V2 classifier is implemented. The contrast is varied from 0 (min) to 1 (max). Beyond a contrast value of 0.2, the algorithm is able to detect the image with a probability of 99.94% (see Table 3).  
Therefore, the analysis is made for the range of 0 to 0.2. Beyond 0.2, the algorithm reaches a saturation threshold and has no effect on POD characterization. We examine 100 contrast values between 0 and 0.2 and the corresponding probability values.  
A graph of the data is illustrated in Figure 8. The inspection threshold (least detectable target), saturation threshold (maximum detectable target), and decision threshold (threshold below which the observed data are characterized as noise) are constructed.  
\begin{table}
\begin{tabular}{l l} \hline
**Contrast** & **Probability** \\ \hline
0 & 0.31452537 \\
0.1 & 0.72617805 \\
0.2 & 0.9944179 \\
0.3 & 0.9994198 \\
0.4 & 0.99984 \\
0.5 & 0.9999324 \\
0.6 & 0.99995387 \\
0.7 & 0.99997306 \\
0.8 & 0.99997616 \\
0.9 & 0.9999759 \\
1 & 0.9999771 \\ \hline \end{tabular}
\end{table}
Table 3: Probability of Detection considering different image contrasts.  
Figure 7: Input images with varying levels of brightness and contrast provided to the classifying networks.  
To establish a POD characterization illustrating the effect of the process parameter, contrast values are examined. A regression analysis is carried out for the dataset. The 95% confidence bounds and the prediction bounds are also constructed (see Figure 9).  
The confidence bounds are constructed for certification purposes while the prediction bounds serve as boundaries so that for every new 100 observations, 95 should fall within. The cumulative density functions (CDF) for the data distribution are also constructed. The POD curve is generated using area of the cumulative density function above the decision threshold (Figure 10).  
Figure 8: Data distribution for logarithmic scale.  
Figure 10: POD generation approach.  
Figure 9: Regression analysis.  
The confidence bounds about the regression line are used in constructing the 95% bounds around the POD curve.  
The 90/95 certification criteria representing a 90% probability of detecting the image with a reliability of 95% is utilized in this contribution. The 90/95 (o in Figure 11) value for this concrete example is 0.02167. This implies that, in varying the contrast values at 0.02167, the algorithm is able to detect the image with 90% probability at 95% reliability. This developed approach provides a new evaluation and performance assessment for classifiers incorporating the effect of image property on the classification results. From Figure 10, it is evident that the probability density for the classifier changes with the parameter (here: contrast). This visualization is not possible using the ROC or PR curve. However, the POD curve does not give any indication of the false call for any selected decision threshold. To compute the false call, noise will be analyzed in the next section.