# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Ablation Study  
In order to verify the effectiveness of the three improvement modules proposed in this paper, we use YOLOv8s as a benchmark model for comparison purposes and train on the DOTAv1.0 dataset under the same experimental environment to obtain the test data for each improvement module, the results of which are shown in Table 3, where \"Base\" denotes the original YOLOv8s model, \"\(\surd\) \" denotes the added module, and \"\(\times\)\" denotes that the module was not added. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Module** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]:0.95\%** \\ \hline CBAM & 83.9 & 77.2 & 82.5 & 54.9 \\ GAM & 84.3 & 77.2 & **82.7** & 54.8 \\ ECA & 83.8 & 77.1 & 82.1 & 54.5 \\ EMA & **84.5** & **77.3** & **82.7** & **55.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of different attention modules. Figure 9: Comparison of different loss functions. As seen from the ablation experiment results in Table 3, each of the improvement modules proposed in this paper has a role in improving the detection accuracy. For DOTA v1.0, the [EMAIL_ADDRESS]% is improved by 0.4% over the original algorithm when the small target detection layer is added to YOLOv8s alone, and its accuracy is improved by 0.8% when Wise-IoUv3 is introduced alone. When both are introduced together, the [EMAIL_ADDRESS]% is improved by 1.1% over the original algorithm, and the detection accuracy is improved for all categories of small targets except plane. When all three modules are added, the [EMAIL_ADDRESS]% of our algorithm is improved by 1.3% compared with that of the original YOLOv8s, indicating that the improved algorithm helps to enhance the model's ability to extract the feature details of the remote sensing image targets, which, in turn, improves the overall detection accuracy of the model. The curves of the experimental results comparing the original algorithm and the improved algorithm on the DOTAv1.0 are shown in Figure 10. The figure shows that the [EMAIL_ADDRESS] of the improved algorithm gradually increases relative to that of the original YOLOv8 algorithm as the number of training epochs increases. In addition, we use the original YOLOv8 algorithm and the improved algorithm proposed in this paper for target detection of four types of remote sensing images. The first type contains dense targets, the second type contains small targets, the third type contains multi-scale targets, and the last type contains targets in complex scenes. The specific results are shown in Figure 11. For dense target images, the original algorithm misses one, and the improved algorithm detects all; for small target images, the original algorithm misses two, and the improved algorithm detects all; for multi-scale target images, the original algorithm misses three, and the improved algorithm detects all; and for targets in complex scenes, the original algorithm misses one, and the improved algorithm detects all. Neither  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{ \begin{tabular}{c} **Layer for** \\ **Small Target** \\ \end{tabular}  } & \multirow{2}{*}{**Wise-**} & \multirow{2}{*}{**EMA**} & \multirow{2}{*}{ \begin{tabular}{c} **Small** \\ **Vehicle** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Large** \\ **Vehicle** \\ \end{tabular}  } & \multirow{2}{*}{**Plane**} & \multirow{2}{*}{ \begin{tabular}{c} **Storage** \\ **Tank** \\ \end{tabular}  } & \multirow{2}{*}{**Ship**} & \multirow{2}{*}{**Harbor**} & \multirow{2}{*}{**[EMAIL_ADDRESS]\%**} \\ \hline \multirow{4}{*}{DOTA} & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 64.6 & 86.4 & 91.8 & 71.5 & 89.2 & **84.9** & 81.4 \\  & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 67.1 & 86.3 & 91.9 & 75.4 & 89.3 & 81.  
of the algorithms incorrectly detects targets in the any of the four types of images.