# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 5 Conclusions  
Compared with existing object detection models for EPOVR, the advancements achieved in our work can be summarized as follows. Firstly, a DC2f module is introduced to mitigate the limited feature representation for objects with irregular shapes. Secondly, an AMFE module is introduced to overcome the limitations in feature representation for objects of various scales. Thirdly, an OCIoU bounding box regression loss is introduced to address the issue of inadequate object localization. Fourthly, an object detection dataset tailored for EPOVR is constructed to address the limited category diversity in existing datasets. Compared to previous studies, this paper primarily differs in three aspects: the proposed DC2f module, AMFE module, and OCIoU loss function, none of which have appeared in prior work. The DC2f module improves feature extraction for irregularly shaped objects by using deformable convolution to dynamically adjust sampling positions and weights, while leveraging depthwise separable convolution to lower the number of parameters and reduce computation overhead. The AMCA module combines multi-scale depthwise separable convolution, adaptive convolution, and channel attention mechanisms to enhance multi-scale feature extraction while maintaining low parameter complexity. The OCIoU loss enhances object localization accuracy by separately optimizing width and height, rather than relying on aspect ratio optimization. On the EPOVR-v1.0 dataset, ablation studies validate the effectiveness of DC2f, AMFE, and OCIoU, both individually and in combination. Comparative experiments with seven mainstream YOLO models demonstrate that our approach achieves the highest detection accuracy while maintaining real-time performance, confirming its overall efficacy.  
Currently, the DAO-YOLO model can only detect the object categories listed in Table 1. However, the architecture of DAO-YOLO model has potential practical values, i.e, it can be applied in other scenarios, such as security monitoring, intrusion detection, etc.  
Future researches will focus on two aspects: (1) enhancing the capability of contour feature extraction, (2) expanding the object categories of EPOVR-v1.0 dataset to cover more scenarios of EPOVR.  
Conceptualization, X.Q.; formal analysis, X.Q.; funding acquisition, W.W.; methodology, X.Q. and Y.L.; project administration, P.X.; resources, W.W.; software, Y.L., X.D. and L.L.; supervision, W.W.; validation, W.W. and J.G.; writing--original draft, Y.L.; writing--review and editing, X.Q. and P.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Henan (Grant No. 252300421063), the Research Project of Henan Province Universities (Grant No. 24 ZX005), the National Natural Science Foundation of China (Grant No. 62076223) and the Third Batch of Science and Technology Projects for Production Frontline of State Grid Jiangsu Electric Power Co., Ltd. in 2023 (Grant No. DL-2023Z-198).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
DC2f Deformable C2f  
AMFE Adaptive Multiscale Feature Enhancement