## 4 Experiment and Analysis
### Evaluation Indicators  
In order to effectively evaluate the effect of low-contrast image before and after enhancement, this paper used the structural similarity (_SSIM_), mean squared error (_MSE_), \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Classification** & **Excavating** & **Loader** & **Lorry** & **Loess** & **Gobbet** & **Cobblestone** & **Fine Sand** & **Paling** & **Cone** & **Total** \\ \hline Number of & & & & & & & & & & \\ images & 752 & 1118 & 2124 & 2015 & 4565 & 2021 & 6109 & 4591 & 2377 & 25,672 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of data set images. Figure 4: Algorithm’s flow chart. and peak signal-to-noise ratio (\(PSNR\)) to evaluate the image after the self-order enhancement. At the same time, on the basis of image enhancement, this paper used the accuracy, _recall_, \(F1\) score, and average accuracy (\(mAP\)) to evaluate the detection accuracy of different algorithms and drew PR curves to compare and analyze the results with those of other algorithms. Among them, the mathematical evaluation model of the color scale enhancement is as follows [26;27]:  
\[\left\{ \begin{array}{l}E_{F}=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}F(i,j),E _{F^{\prime}}=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}F^{\prime}(i,j)\\ \mu_{F}=\sqrt{\frac{1}{MN-1}\sum_{i=1}^{M}\sum_{j=1}^{N}[F(i,j)-E_{F}]^{2}}\\ \mu_{F^{\prime}}=\sqrt{\frac{1}{MN-1}\sum_{i=1}^{M}\sum_{j=1}^{N}[F^{\prime}(i, j)-E_{F}]^{2}}\\ \mu_{F}=\frac{1}{MN-1}\sum_{i=1}^{M}\sum_{j=1}^{N}[F(i,j)-E_{F}][F^{\prime}(i, j)-E_{F^{\prime}}]\\ SSIM=\frac{\left(2E_{F}E_{F^{\prime}}+\varepsilon_{1}\right)\left(2\varepsilon_{ F^{\prime}}+\varepsilon_{2}\right)}{\left(E_{F}^{2}+E_{F^{\prime}}^{2}+ \varepsilon_{1}\right)\left(\varepsilon_{F}^{2}+\varepsilon_{2}^{2}\right)} \end{array} \right. \tag{22}\]  
where \(M\) and \(N\) are the image row and column numbers; \(F\) is the original image; \(F^{\prime}\) is the predicted image; \(E_{F}\) is the mean value of the original image; \(E_{F^{\prime}}\) is the mean value of the predicted image; \(\mu_{F}\) is the standard deviation of the original image; \(\mu_{F^{\prime}}\) is the standard deviation of the predicted image; \(\mu_{FF}\) is the covariance of the original image and the predicted image; and \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are constants, generally 0.01. \[MSE=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\left[F(i,j)-F^{\prime}(i,j)\right] ^{2} \tag{23}\]  
where \(M\) and \(N\) are the image row and column numbers; \(F\) is the original image; and \(F^{\prime}\) is the predicted image. The peak signal-to-noise ratio model expression is as follows [28]:  
\[PSNR=10\log_{10}\frac{(2r-1)^{2}}{MSE}=20\log_{10}\frac{2r-1}{MSE} \tag{24}\]  
where \(r\) represents the pixel value of the image and \(MSE\) represents the mean squared error. Among the target detection accuracy evaluation indicators, the accuracy, _recall_, \(F1\) score, and average accuracy (\(mAP\)) evaluation metrics are as follows [29]:  
\[Recall\ =\frac{TP}{TP+FN} \tag{25}\]  
\[Precision=TP/\left(TP+FP\right) \tag{26}\]  
\[F1=2(precisionrecall)/\left(precision+recall\right) \tag{27}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{28}\]  
\[mAP=\frac{1}{C}\sum_{i=1}^{C}AP_{i} \tag{29}\]  
In the formula, \(TP\) denotes the number of actual positive samples among the targets detected by the algorithm, \(FP\) denotes the number of actual negative samples among the targets detected by the algorithm, and \(FN\) denotes the number of targets missed by the algorithm. _Precision_ reflects the proportion of the real targets in the detection results, and the higher the value, the more accurate the detection effect is. _Recall_ reflects the proportion of the real targets detected, and the higher the value, the more comprehensive the detection effect is. _Recall_ reflects the proportion of real targets detected, the higher the value, the more comprehensive the detection effect. The \(F1\) score represents a balanced index between model _precision_ and _recall_. The higher the value, the better the model.