## 5 Conclusions  
To create an innovative solution to the challenges of background noise interference and target occlusion loss information in thermal infrared images, this study proposed an infrared object detecting algorithm with a basis in deep learning with efficient feature extraction in complex scenes. With superior accuracy and robustness compared to other mainstream models in this field, this algorithm effectively overcomes some of the obstacles posed by thermal infrared images. To achieve these results, our proposed method utilizes an ISW module to expand the perception field of the fusion network for input data, which is beneficial for dealing with different sizes and occluded targets in infrared images. Additionally, TAHNet enables the detection head to better handle various shapes and poses, further enhancing the model's robustness. Finally, the C2f module optimizes the feature extraction process, comprehensively capturing different features in the image and more accurately identifying the infrared target.  
Infrared target detection remains a dynamic research area with vast potential for exploration. The algorithm presented in this study has strong feature expression capacity; in the future, our primary research emphasis will be on developing a lightweight network framework, aiming to attain a harmonious balance between exceptional precision and swift computation. Simultaneously, we intend to validate the detection performance across a broader spectrum of infrared target datasets. With advancing technology and  
Figure 12: Visual effects of detection based on original markers and the proposed model on KAIST pedestrian dataset.  
the development of more practical applications, we expect to see increasingly advanced systems that accurately detect infrared targets in real time.  
Conceptualization, J.Y. and H.W.; data curation, H.W. and S.L.; validation, J.Y., H.W. and S.L.; software, H.W. and S.L.; methodology, J.Y. and H.W.; formal analysis, J.Y. and S.L.; resources, J.Y.; investigation, S.L. and S.Z.; writing--original draft preparation, H.W.; visualization, H.W. and S.Z.; writing--review and editing, J.Y., H.W., S.L. and S.Z.; funding acquisition, J.Y.; project administration, S.Z.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EEG recognition and service robot control based on structure optimization deep network in the background of high noise, grant number 61673079.  
Publicly available datasets were analyzed in this study. The FLIR dataset can be found at [[https://www.flir.com/oem/adas/adas-dataset-form/](https://www.flir.com/oem/adas/adas-dataset-form/)]([https://www.flir.com/oem/adas/adas-dataset-form/](https://www.flir.com/oem/adas/adas-dataset-form/)), accessed on 29 August 2022. The data presented in this study are openly available in KAIST at [[https://www.doi.org/10.11](https://www.doi.org/10.11)]([https://www.doi.org/10.11](https://www.doi.org/10.11)) 09/CVPR.2015.7298706, reference number [36].  
The authors express their gratitude to [PERSON] for providing assistance with computing resources.  
The authors declare no conflict of interest.