## 4 Experiments and Analysis
### Comparison of Small Object Test Results  
For this test, a total of 106 images containing small objects were selected based on the object scale in the VOC 2007 dataset. The selected objects are small and densely gathered, and most are approximately 1/10 of the image size in scale. In total, the images contain 960 ground-truth objects. Comparisons are presented between the Multi-view YOLO and classical YOLO methods and between the Multi-view SSD and classical SSD methods. Notably, an updated version of YOLO has also been released: YOLOv2 [27]. YOLOv2 has a detection speed of 67 fps on a single Nvidia Titan X graphics card and a mAP of 78.6% on the VOC 2007 test set. To obtain a rich set of comparison results, experiments were conducted on YOLO, YOLOv2, and SSD. The selected object classes include aero, bird, boat, bottle, car, chair, cow, dog, horse, person, potted plant and sheep. By synthetically considering the two aspects of object confidence and object number, the YOLO detection threshold was set to 0.2, and the detection thresholds for both YOLOv2 and SSD were set to 0.4. Typical detection results are shown in Figures 5-7. Figures 5-7 show that for small object detection, the multi-view methods exhibit the following three characteristics compared with the classical methods. First, the multi-view methods detect more objects; second, they achieve greater confidence when recognizing the same object; and third, they can correctly identify objects that are incorrectly identified by the classical methods. Classical YOLO did not detect objects in ten images, whereas Multi-view YOLO did not detect objects in only two images. Classical YOLOv2 did not detect objects in four images, and classical SSD did not detect objects in six images, whereas Multi-view YOLOv2 and Multi-view SSD successfully detected objects in all images. These results show that the multi-view methods achieve better performance in small object detection. Figure 5: Comparisons of small object detection results between the classical You Only Look Once (YOLO) method (the first and third columns) and the Multi-view YOLO method (the second and fourth columns). Figure 6: Comparisons of small object detection results between the classical YOLOv2 method (the first and third columns) and the Multi-view YOLOv2 method (the second and fourth columns). Figure 7: Comparisons of small object detection results between the classical SSD method (the first and third columns) and the Multi-view SSD method (the second and fourth columns). Figures 8-10 show that the retrieval abilities of the multi-view methods are mostly higher than those of the classical methods. When calculated as the average retrieval results for all 106 images, the AF (average F-measure) scores of Multi-view YOLO and classical YOLO are 0.496 and 0.319, respectively; the AFs of Multi-view YOLOv2 and classical YOLOv2 are 0.621 and 0.561, respectively; and the AFs of Multi-view SSD and classical SSD are 0.729 and 0.560, respectively. In Figures 8-10, a zero point on a curve represents an F-measure value of zero for the corresponding image, which means that the model retrieved no objects that satisfied the retrieval condition. An F-measure value of zero is observed in two situations: when no object is detected in the image and when none of the objects in the detection results satisfies the IoU (Intersection over Union) condition (IoU \(>\) 0.5). Corresponding to the first situation, classical YOLO detected no objects in images 3, 33, 36, 46, 68, 74, 89, 95, 97, and 104; Multi-view YOLO detected no objects in images 6 and 97; classical YOLOv2 detected no objects in images 3, 4, 33 and 68; and classical SSD detected no objects in images 6, 32, 46, 56, 71, and 97.