## 5 Experiments
### Experimental Parameters  
According to the characteristics of the detected image and the above parameter setting analysis, the optimization of the parameter setting is divided into the following three parts:  
1. The first part is the setting of the model parameters. The setting of the model parameters includes the number of categories, the feature extraction network-related settings, the first candidate region generation network setting, the first prediction network hyper parameter setting, non-maximum value suppression setting, loss function parameter setting, pooling kernel parameter setting, second prediction network hyper parameter setting, second bounding box regression parameter setting, positive and negative sample screening IoU threshold, and so on. The specific parameter settings are shown in Table 2.  
2. The second part is the setting of training parameters. The training parameter setting includes one training data point, the optimization method, moving average, model file saving path and so on. The specific parameter settings are shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Setting Values** & **Remarks** \\ \hline num\_class & 4 & Number of categories \\ \hline feature\_extractor & resnet101 & Feature extraction network \\ \hline  & Baseanchorsize = [64; 64] & \\ first\_stage\_anchor\_generator & scales: [0.5, 1.0, 2.0] & \\ first\_stage\_anchor\_generator & aspect\_ratios: [0.5, 1.0, 2.0] & Anchor size adjustment \\  & height\_stride: 16 & \\  & width\_stride: 16 & \\ \hline first\_stage\_box\_predictor\_ & 12\_regularizer & \\ conv\_hyperparams & twentieth\_0.0 & RPN convolution network \\  & truncated\_normal\_initializer & hyperparameter setting \\ \hline first\_stage\_nms\_score\_threshold & 0.0 & RPN confidence non-maximum \\  & suppression threshold \\ \hline first\_stage\_nms\_loU\_threshold & 0.0 & RPN cross ratio non-maximum \\  & suppression threshold \\ \hline first\_stage\_max\_proposals & 300 & Maximum number of candidate areas \\ \hline first\_stage\_localization\_loss\_weight & 2.0 & RPN positioning loss weight \\ \hline first\_stage\_objectness\_loss\_weight & 1.0 & RPN object classification loss weight \\ \hline initial\_crop\_size & 14 & Initialize the crop size \\ \hline maxpool\_kernel\_size & 2 & Maximum pooling core size \\ \hline maxpool\_stride & 2 & Maximum pooling nuclear step \\ \hline  & use\_dropout: false & \\  & dropout\_keep\_probability: 1.0 & \\ second\_stage\_box\_predictor & 12\_regularizer & Classification network \\  & weight: 0.0 & hyperparameter setting \\  & truncated\_normal\_initializer & \\  & stddev: 0.01 & \\ \hline  & score\_threshold: 0.0 & \\ second\_stage\_post\_processing & max\_detections\_per\_class: 100 & Classification network post processing \\  & max\_total\_detections: 300 & parameter settings \\  & score\_converter: SOFTMAX & \\ \hline second\_stage\_localization\_loss\_weight & 2.0 & Classification network positioning \\  & loss weight \\ \hline second\_stage\_classification\_loss\_weight & 1.0 & Classification network classification \\  & loss weight \\ \hline roi\_positive\_ratio & 0.3 & Positive sample cross ratio threshold \\ \hline roi\_negative\_ratio & 0.1 & Negative sample intersection ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter settings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Setting Values** & **Remarks** \\ \hline batch\_size & 5 & Batch size \\ \hline  & initial\_learning\_rate: 0.0003 & \\ Adam\_optimizer\_value & step: 30000 & Adam optimizer parameters \\  & momentum\_optimizer\_value: 0.9 & \\ \hline use\_moving\_average & True & Uses moving average parameters \\ \hline gradient\_clipping\_by\_norm & 10.0 & Gradient cropping \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameter settings. The third part is the setting of the evaluation parameters. The evaluation parameter settings include the number of evaluation samples, the evaluation data input path, the label path, and so on.