# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 4 Experimental Evaluation
### Comparison with Other Methods  
In order to validate the efficacy of the proposed method in detecting tiny objects, some notable networks, including YOLOv3-spp [55], YOLOv5s, Faster R-CNN, ATSS [56], Cascade R-CNN [57], a tiny object detection method from the literature [21], and the original YOLOv8s algorithm were chosen. The AI-TOD dataset was employed as the experimental data, and the outcomes of these experiments are presented in Table 1.  
For the Faster R-CNN algorithm, this method selects candidate regions by RPN from the features extracted from the backbone network in the first stage. It extracts information from these candidate regions for detection in the second stage. mAP0.5 and mAP0.5:0.95 are both lower, and the FPS is only 16. For ATSS and Cascade R-CNN algorithms, although both are close to mAP0.5 and mAP0.5:0.95, and both have higher accuracy than Faster R-CNN, both have extremely low FPS and more complex models with more parameters than Faster R-CNN. For the YOLOv3-spp algorithm, this method uses the SPP structure after the backbone network of the YOLOv3 network to enhance the fusion effect of feature maps at different scales to achieve tiny object detection. Due to this method's poor feature extraction effect and feature fusion, it is not easy to extract and fuse tiny object features in complex backgrounds. Despite the FPS of 74, it has 41.1% for mAP0.5 and 18.6% for mAP0.5:0.95, which are reduced by 2.3% and 0.7%, respectively, compared with the YOLOv8s algorithm. The YOLOv5s algorithm uses an efficient aggregation network PANet to replace the feature extraction network in the original YOLOv3 network to improve recognition accuracy and speed. Although the method achieves a recognition speed of 102 FPS, it is difficult to achieve better detection results using this algorithm due to interference in the form of a lack of fine-grained feature information of tiny objects in complex background tiny object images. Except for the Faster R-CNN and YOLOv3-spp algorithms, its mAP0.5 and mAP0.5:0.95 are lower than other comparative methods. Although the YOLOv8s algorithm achieves 43.4% for mAP0.5 and 19.3% for mAP0.5:0.95, with a recognition speed of 94 FPS, the accuracy is lower than that of the method used in the literature [21], and the trade-off between recognition accuracy and speed is not well obtained. The method used in the literature [21] was combined with Cascade R-CNN by replacing the original IOU metric with the NWD metric to enhance the accuracy rate of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mAP0.5/\%** & **mAP0.5:0.95\%** & **FPS** &
\begin{tabular}{c} **Model** \\ **Size/MB** \\ \end{tabular} \\ \hline Faster R-CNN [13] & 26.3 & 11.1 & 16 & 236.33 \\ ATSS [56] & 30.6 & 12.8 & 2 & 244.56 \\ Cascade R-CNN [57] & 30.8 & 13.8 & 1 & 319.45 \\ YOLOv3-spp [55] & 41.1 & 18.6 & 74 & 29.97 \\ YOLOv5s & 42.2 & 18.6 & 102 & 17.67 \\ YOLOv8s & 43.4 & 19.3 & 94 & 21.48 \\ Literature [21] & 49.3 & 20.8 & 8 & 942.92 \\ Proposed algorithm & 48.3 & 22.7 & 37 & 19.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of different methods on AI-TOD dataset. complex background tiny object recognition. Although the mAP0.5 of this method reached 49.3% and mAP0.5:0.95 reached 20.8%, the model size of this method reached 942.92 MB, while the FPS was only 8. This method could be more favorable for practical deployment applications. The proposed method combines the path aggregation network SPANet, redesigned YOLOv8s feature extraction network, and uses the SPD-Conv module to make the network retain more feature information. Compared with the original YOLOv8s algorithm, mAP0.5 improves by 4.9%, mAP0.5:0.95 improves by 3.4%, and model parameters are reduced, and compared with the algorithm in the literature [21], mAP0.5 differs little, mAP0.5:0.95 improves by 1.9%, FPS improves by 29, and model parameters are significantly reduced, model size reduced 46 times, thus achieving a better detection precision and speed trade-off.