# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 5 Conclusions  
This research introduced an enhanced YOLOv7-CA dynamic detection model to address the challenge of recognizing small targets effectively. By incorporating the principles of separation and merging, we developed the SPPCSPC module to meticulously extract intricate details from images. Additionally, we introduced the CA module, synergizing channel attention mechanisms with spatial attention methods. Building upon these advancements, we further introduced a dynamic convolutional module to counteract the false detections and missed detections that stem from significant variations in small target sizes and pronounced background interference. This dynamic convolution module adapted to diverse target sizes and backgrounds, consequently substantially enhancing detection accuracy. Empirical evidence corroborates that the improved YOLOv7-CA dynamic network model surpasses both the standard classical target-detection network model and the original network model in terms of accurate target detection.  
Nonetheless, it is important to acknowledge the limitations inherent in the model presented in this study. For instance, the dataset's sample size remains relatively small, and the diversity of background variations among the targets is limited. Moreover, the model we proposed is characterized by a considerable number of parameters and computations, which can demand a significant amount of computational resources for its practical implementation.  
Given the relatively modest number of categories encompassed by the dataset used in this paper, our future endeavors will be directed towards its augmentation. We are committed to progressively expanding the detection categories within the dataset, thereby broadening the spectrum of detectable entities and augmenting the algorithm's overall applicability. Moreover, we envisage future efforts focused on lightweighting the model. This strategic initiative aims to optimize the model for reduced computational demands during real-world deployment, effectively bolstering the algorithm's performance and applicability across diverse practical scenarios. By doing so, we anticipate a heightened capability of the model to address various small-target-detection tasks.  
Conceptualization, K.L., Y.W. and Z.H.; data curation, K.L., Y.W. and Z.H.; methodology, K.L. and Y.W.; writing--original draft, K.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shaanxi Province, China (No. 2020 JM-499 and No. 2020 JQ-684) and the National Natural Science Foundation of China (No. 61803294).  
Not applicable.  
The datasets used in this paper can be downloaded from the website: [[http://www.orca-tech.cn/datasets](http://www.orca-tech.cn/datasets)]([http://www.orca-tech.cn/datasets](http://www.orca-tech.cn/datasets)).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Image Size (px)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.9** \\ \hline YOLOv4 & 720 \(\times\) 720 & 0.537 & 0.206 \\ YOLOv5 & 720 \(\times\) 720 & 0.561 & 0.243 \\ YOLOv7 & 720 \(\times\) 720 & 0.759 & 0.331 \\ FCOS [32] & 720 \(\times\) 720 & 0.437 & 0.164 \\ VFNet [33] & 720 \(\times\) 720 & 0.551 & 0.237 \\ TOOD [34] & 720 \(\times\) 720 & 0.511 & 0.219 \\ Ours & 720 \(\times\) 720 & 0.811 & 0.381 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._