# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Ablation Study  
To assess the roles of the CA module and the action module in SlowFast and to visually demonstrate the improvement of the enhanced model in detecting various behaviors, ablation experiments were conducted on each SlowFast model. The results for the detection of the seven behavior categories were compared. Table 3 presents the outcomes of the ablation experiments, where \(\surd\) indicates that the module was used, and \(\times\) indicates that the module was not used. The line graph depicted in Figure 15 illustrates the performance variations of each model throughout the experiment. In this graph, critical moments during model training are recorded every five rounds of experiments. Each folded line represents the performance curve of the corresponding model. By examining these curves, we can comprehend the model's performance in different training phases and obtain detailed timing information regarding the experimental results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CA**} & \multirow{2}{*}{**ACTION**} & \multicolumn{6}{c}{**Category of [EMAIL_ADDRESS]**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} \\ \cline{3-3} \cline{5-10}  & & & **Farm** & & **Manure** & **Seed** & **Sow** & **Spray** & **Water** & **Weed** \\ \hline
1 & \(\times\) & \(\times\) & 74.19 & 95.92 & 93.37 & 94.17 & 73.80 & 86.99 & 75.25 & 84.78 \\ \hline
2 & \(\surd\) & \(\times\) & 76.63 & 98.15 & 96.44 & 96.91 & 81.82 & 91.55 & 80.63 & 88.74 \\ \hline
3 & \(\times\) & \(\surd\) & 80.28 & 96.83 & 97.39 & 95.05 & 83.13 & 93.47 & 84.14 & 90.04 \\ \hline
4 & \(\surd\) & \(\surd\) & 84.28 & 98.54 & 97.96 & 97.93 & 84.09 & 94.10 & 86.15 & 91.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative results of ablation experiment (%). From Table 3, it is evident that the original model (Model 1) exhibits an average recognition performance for \"farm\", \"spray\", and \"weed\" behaviors, with an overall average accuracy (mAP) of 84.78%. In Model 2, which introduces the CA module, the overall recognition accuracy improves. Notably, the average accuracy of \"spray\" behavior increases by 8.02%, \"weed\" behavior improves by 5.38%, and \"water\" behavior improves by 4.56%. These improvements indicate that the CA attention module effectively extracts crucial information related to action and morphological features by assigning weights to the channels of the feature map. This strengthens the model's capacity to learn key features and enhances target differentiation, leading to an overall model accuracy improvement. The mAP increases by 3.96% compared to the original model, underscoring the effectiveness of the CA module in extracting key feature information. This outcome supports the efficacy of the CA module in extracting crucial feature information. In Model 3, where the ACTION module is introduced into the fast branch, there is an overall improvement in recognition accuracy, particularly for behaviors with high temporal feature requirements. Notably, the average accuracy for the \"farm\" behavior improves by 6.09%, and the \"weed\" behavior sees a significant improvement of 8.89%. This highlights the advantage of the ACTION module in integrating temporal and spatial information. The module ensures more comprehensive feature extraction of temporal information, not only emphasizing the spatial features of the image but also capturing changes in these features over time. In tasks requiring the capture of dynamic target features, such as behavior recognition or object tracking in a video, the inclusion of the ACTION module enhances the model's ability to understand and capture subtle temporal changes. Consequently, the overall mAP improves by 5.26% compared to the original model. Comparing Model 2 with Model 3, it is observed that, while Model 2 is not as effective as Model 3 overall, the CA module exhibits greater efficacy than the ACTION module in the behavioral categories of \"manure\" and \"sow\".