# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 5 Conclusions  
In this study, we employ the Faster-RCNN + SlowFast spatio-temporal action detection algorithm for the purpose of detecting manual labor behavior in agricultural fields. Additionally, we curate a custom dataset named the Field Manual Labor Behavior Dataset (FWBD). The Faster-RCNN model is employed to identify personnel targets within key frame images. Subsequently, the detected targets are input into the SlowFast model's fast and slow channels based on varying frame counts, facilitating the accomplishment of spatio-temporal action recognition tasks. The SlowFast model undergoes innovative enhancement through the incorporation of the CA attention module and ACTION attention module, resulting in improved recognition  
Figure 19: Resulting graph of “weed” behavior detection of video frames. Figure 17: Resulting graph of “water” behavior detection of video frames. Figure 20: Resulting graph of “spray” behavior detection of video frames. accuracy compared to the original model. Although the improved model has more parameters, the increase in FLOP compared to the original SlowFast model is not substantial. This indicates that while the CA-ACTION-SlowFast model may have increased the complexity to some extent, it did not significantly increase the computational load during the inference phase. This is beneficial for maintaining the efficiency and utility of the model in practical applications. Furthermore, experimental findings reveal that the CA-ACTION-SlowFast model enhances the mAP by 7.08% in comparison to the original model. The notable performance of the CA-ACTION-SlowFast model underscores the potential for enhancing behavioral detection accuracy through the integration of spatial and dynamic features. Leveraging channel attention and temporal attention proves effective in bolstering the recognition of key features. The substantial improvement emphasizes the significance of the CA and ACTION modules. The model's capability to prioritize more informative channels proves to be a viable strategy, particularly in real-world scenarios with disturbances. This strategy assists the model in filtering out irrelevant features, allowing it to focus on components critical to the recognition task. Ultimately, the CA-ACTION-SlowFast model offers a viable solution for practical applications, notably in the realm of detecting manual labor behaviors in agricultural settings. The CA and ACTION modules may improve the performance of existing systems by providing more granular contextual information and action recognition capabilities, and can add additional layers and depth to improve overall system understanding and responsiveness. This advancement promotes the digitization and standardization of farm workers, driving traditional agriculture toward further automation and smart farming, which is of significant importance. Future work will focus on the following aspects. First, integrating the improved network into lightweight monitoring devices. This integration has the potential to effectively capture behavioral events, significantly enhancing the functionality and utility of monitoring devices, and providing valuable information for traceability systems. Second, further exploring the model's structure and conducting experiments on other publicly available datasets to compare results aim to improve the model's generalization capability and increase the model's evaluation metrics to improve its reliability for real-world applications. Eventually, this will enrich the quantity and diversity of datasets. Only large-scale datasets can effectively validate the model's performance. Additionally, including videos from different weather conditions and different times of day in each category can enhance the model's robustness against changes in lighting and climate. Methodology, M.Z. ; software, M.Z.