## 4 Experiments and Analysis
### Comparison of Object Detection Accuracy  
In object detection, the mAP (mean average precision) metric is typically adopted to evaluate the detection accuracy. For the 106 images introduced above, the mAP values of the classical methods and the corresponding multi-view methods were calculated with the confidence thresholds for YOLO, YOLOv2 and SSD set to 0.2, 0.4 and 0.4, respectively. The results are shown in Table 2.  
Figure 10: Comparison of the object retrieval abilities of the Multi-view SSD and classical SSD methods. Table 2 shows that the mAP scores of the multi-view methods are higher than those of the classical methods in every object class. When difficult objects are not included, the mAP of Multi-view YOLO is 38.6%, which is higher than that of classical YOLO by 14.3%; the mAP of Multi-view YOLOv2 is 56.2%, which is higher than that of the classical YOLOv2 by 7.4%; and the mAP of Multi-view SSD is 64.4%, which is higher than that of classical SSD by 13.1%. When difficult objects are included, the mAP of Multi-view YOLO is 30.0%, which is higher than that of classical YOLO by 12.1%; the mAP of Multi-view YOLOv2 is 40.6%, which is higher than that of classical YOLOv2 by 5.9%; and the mAP of Multi-view SSD is 45.3%, which is higher than that of classical SSD by 9.0%. Hence, the multi-view methods achieve higher accuracy than the classical models for small object detection. However, neither the classical YOLO method nor the Multi-view YOLO method correctly detects horses because horse images are erroneously identified as cows; this behavior is related to the training of the YOLO model. Thus, in Table 2, the mAP scores of both classical YOLO and Multi-view YOLO do not include results for the horse class; however, this omission does not affect the algorithm performance comparison. The above experimental results indicate that the multi-view approach proposed in this paper enables a better retrieval capability and a higher detection accuracy for small object detection than can be achieved using the classical models. Comparisons of Detection Performance between Multi-View Methods and State-of-the-Art Methods Based on Region Proposals  
In consideration of the detection results, Multi-view YOLOv2 and Multi-view SSD were chosen for comparison with state-of-the-art methods based on region proposals, including Faster RCNN, R-FCN, and SPP-Net. The mAPs of Faster RCNN, R-FCN, and SPP-Net with a confidence threshold of 0.4 are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{8}{c}{**AP (\%)**} \\ \cline{3-14}  & & **Aero** & **Bird** & **Boat** & **Bottle** & **Car** & **Chair** & **Cow** & **Dog** & **Horse** & **Person** & **Plant** & **Sheep** \\ \hline YOLO & 24.3 & 20.2 & 19.9 & 7.4 & 3.9 & 29.8 & 19.4 & 44.2 & 37.2 & / & 24.3 & 20.3 & 40.6 \\ Multi-view YOLO & 38.6 & 46.9 & 32.0 & 34.6 & 20.5 & 52.4 & 39.7 & 39.7 & 43.1 & / & 26.6 & 30.2 & 59.3 \\ YOLO* & 17.9 & 18.9 & 12.6 & 3.5 & 3.3 & 22.1 & 14.7 & 28.9 & 30.2 & / & 14.9 & 16.4 & 31.1 \\ Multi-view YOLO* & 30.0 & 44.1 & 20.3 & 16.5 & 17.2 & 38.9 & 30.2 & 41.5 & 35.0 & / & 16.3 & 24.4 & 45.4 \\ YOLOv2 & 48.8 & 40.0 & 40.0 & 38.2 & 31.5 & 65.4 & 52.0 & 75.4 & 51.9 & 28.6 & 55.8 & 39.1 & 68.1 \\ Multi-view YOLOv2 & 56.2 & 68.1 & 40.7 & 50.1 & 49.4 & 80.1 & 57.9 & 71.4 & 71.0 & 31.4 & 56.5 & 32.6 & 65.3 \\ YOLOv2* & 34.7 & 37.5 & 25.4 & 18.2 & 26.6 & 48.5 & 39.5 & 49.3 & 42.2 & 11.1 & 34.3 & 31.6 & 52.2 \\ Multi-view YOLOv2* & 40.6 & 64.1 & 25.9 & 23.9 & 41.7 & 59.4 & 44.0 & 46.7 & 57.7 & 12.2 & 34.7 & 26.3 & 50.0 \\ SSD & 51.3 & 65.6 & 61.2 & 62.0 & 18.2 & 72.1 & 44.7 & 59.8 & 55.2 & 28.6 As seen from the table, regardless of whether difficult objects are included, the mAPs of Faster RCNN and R-FCN are much higher than those of YOLOv2 and SSD. However, the performance gap is greatly reduced for Multi-view YOLOv2 and Multi-view SSD.