# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 3 Results and Discussion
### Performance Comparison and Discussion
#### 3.3.3 Ablation Study  
The ablation study is applied to analyze the contributions of each component in the model. We divide the entire model into three sub-modules: spatio-temporal module (STM), deep feature module (DFM), and state-aware module (SATM). The first combination is the single STM, which only uses Conv-LSTM cells and flattens the final vector to detect targets. The second combination is a single DFM. The third combination is STM + DFM without a dynamic search strategy. The fourth combination is STM + SATM, and the last combination is DFM + SATM.  
The ablation experiments are implemented on unified datasets. The results are shown in Tables 5 and 6. One can see that multiple-frame detection can improve the accuracy of the algorithm compared to the spatial feature approach alone. It, respectively, improves the detection rate by 12.95% and 17.43% in self-  
temporal feature extractor (STM), deep feature extractor (DFM), and state-aware module (STAM). The STM is trained to weigh the image sequence from the temporal perspective. Subsequently, the DFM is applied to broaden the receptive field, and it fuses multiscale features between the target and the background. In the inference, with the purpose of improving the robustness and practicality, the STAM is used to solve the redundancy calculation, background occlusion, and frame loss encountered. Experimental results reveal that the method obtains higher accuracies and lower false alarms than the baseline algorithms, even over a low SNR.  
This method would hopefully be applied to IRST for anti-UAV remote monitoring and early warnings. Divergent from existing single-frame-based deep learning methods, the proposed method introduces recurrent networks to learn the temporal feature of moving targets. The experiments demonstrate the potential of multi-frame-based neural networks and emphasize the importance of the joint detection of spatial-temporal information. However, the LSTM cell inevitably needs magnanimous training datasets to learn the motion feature, which reduces its usefulness in particular circumstances. In addition, the proposed method still involves manual hyperparameters, such as the length of input frames determined by the motion speed. The manual parameter also limits applications. In the future, self-supervised training without labels will be discussed for improving the network's training and the intelligence of the method.  
Conceptualization, J.L. and X.H.; methodology, J.L., P.L. and X.H.; software, J.L.; validation, J.L. and X.H.; formal analysis, J.L. and P.L.; investigation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, W.C. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Defense Key Laboratory of Science and Technology of Chinese Academy of Sciences, and the grant number is CXJ-21S030.  
Not applicable.  
The public dataset in this paper can be obtained from the website [[https://www.scidb.cn/en/detail?dataSetId=720626420933459968](https://www.scidb.cn/en/detail?dataSetId=720626420933459968)]([https://www.scidb.cn/en/detail?dataSetId=720626420933459968](https://www.scidb.cn/en/detail?dataSetId=720626420933459968)) (accessed on 17 August 2020).  
The authors declare no conflict of interest.