# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 5 Results
### Comparing with the SOTA Lightweight Model GhostNet  
Serving as the feature extraction backbone of the detection model also, we made a comparison between the LP-YOLO and the SOTA lightweight model GhostNet. Based on Table 6, the following observations can be made: Our algorithm outperformed GhostNet as backbone of the model in terms of AP50 and AP50:95 with smaller FLOPs on the landslide dataset. We found that adding the VH block and CBAM module to the backbone resulted in a 0.8% AP50 improvement and a 2% AP50:95 improvement for the YOLOv5-GhostNet model. In correspondence with LP-YOLO, it could achieve a 1.6% and a 0.5% improvement for AP50 and AP50:95, respectively. Furthermore, using the NewPAN structure simultaneously could make the model have better performance with a slight increase in FLOPs. To summarize, in comparison to the original YOLOv5s model, our proposed model in this study exhibited a considerable reduction in the number of parameters and FLOPs, while simultaneously achieving an increase of 2.6% in AP50 and 2.5% in AP50:95. Besides, the proposed model outperformed the mainstream lightweight object-detection algorithms. Specifically, the model attained a detection precision of 49.0% for AP50, 25.5% for AP50:95, and a Latency of 13.5 ms, which fulfilled the real-time detection performance requirements and detection accuracy of landslides, thereby enabling automatic identification of landslides. Figures 10 and 11 depict the detection performance of YOLOv5s and LP-YOLO on sparse landslide images. Figures 10 and 11 (middle) show the detection results obtained by the YOLOv5s model, while Figures 10 and 11 (right) represent the detection results obtained by the proposed LP-YOLO model. Comparing the two models, it is evident that the YOLOv5s model exhibited a higher confidence score, indicating a greater level of confidence in the detected objects. On the other hand, our proposed LP-YOLO model detected landslides with a slightly lower confidence score,, but exhibited a relatively low missed detection rate to strike a balance. Despite the model having a slightly lower \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Neck** & **AP50 (\%)** & **AP50:95 (\%)** & **FLOPs(G)** \\ \hline YOLOv5 & GhostNet & PAN & 45.6 & 22.3 & 7.6 \\ YOLOv5 & GhostNet + VH & PAN & 46.4 & 24.3 & 7.6 \\  & block + CBAM & NewPAN & **47.7** & **25.0** & 7.9 \\  & & & & & \\ LP-YOLO & PartitionNet & PAN & 46.8 & 24.6 & 6.9 \\  & & & & & \\ LP-YOLO & PartitionNet + VH & PAN & 48.4 & 25.1 & 7.1 \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing with the SOTA lightweight model GhostNet. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AP50 (\%)** & **AP50:95 (\%)** \\ \hline SSD & 38.0 & 18.5 \\ Faster RCNN & 42.5 & 21.3 \\ YOLOv3 & 37.7 & 18.1 \\ YOLOv4 & 38.1 & 18.6 \\ YOLOv5-s & 46.4 & 23.0 \\ YOLOv5-m & 48.0 & 25.1 \\ YOLOv-s & 43.6 & 22.8 \\ YOLOv-m & 44.6 & 24.7 \\ YOLOv8-n & 46.0 & 26.3 \\ YOLOv8-s & 44.5 & 26.8 \\ LP-YOLO(ours) & **49.0** & 25.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing with different detection algorithms. confidence score, the LP-YOLO model displayed higher sensitivity, resulting in fewer instances of missing real objects. This indicated that the model achieved a relatively low false positive rate, even in complex situations, and provided more comprehensive detection results. Figures 12 and 13 depict the detection performance of YOLOv5s and LP-YOLO on dense landslide images. The detection results indicated that LP-YOLO exhibited superior performance compared to YOLOv5s for dense landslides. However, it is worth noting that the detection performance of LP-YOLO regarding small-area landslides required further enhancement, as there existed a certain missed detection rate. Figure 11: Real landslide and predicted results of the YOLOv5s and LP-YOLO models. (**left**) Ground truth box for sparse landslide; (**middle**) YOLOv5s prediction; (**right**) LP-YOLO model prediction.