Research Article
Research on Small Target Detection Technology Based on the MPH-SSD Algorithm
Table 4
Performance comparison of the MPH-SSD algorithm and other algorithms on the MS COCO dataset.
| Method | Backbone network | Avg. precision, IoU | Avg. precision, area | Avg. recall, area | IoU = 0.5 : 0.95 | IoU = 0.5 | IoU = 0.75 | Area : S | Area : M | Area : L | Area : S | Area : M | Area : L |
| Faster R-CNN [37] | VGG16 | 24.2 | 45.3 | 23.5 | 7.7 | 26.4 | 37.1 | — | — | — | Mask R-CNN [29] | ResNeXt-101-FPN | 37.1 | 60.0 | 39.4 | 16.9 | 39.9 | 53.5 | — | — | — | YOLOv2 [40] | Darknet19 | 21.6 | 44.0 | 19.2 | 5.0 | 22.4 | 35.5 | 9.8 | 36.5 | 54.4 | SSD512 [40] | VGG16 | 27.7 | 46.4 | 26.7 | 10.9 | 31.8 | 43.5 | 16.5 | 46.6 | 60.8 | DSSD513 [41] | ResNet-101 | 33.2 | 53.3 | 35.2 | 13.0 | 35.4 | 51.1 | 28.9 | 43.5 | 46.2 | DF-SSD [43] | DenseNet-S-32-1 | 29.5 | 50.7 | 31.3 | 9.8 | 31.1 | 46.5 | 17.3 | 46.8 | 64.4 | SEFN512 [44] | VGG16 | 33.7 | 54.7 | 35.6 | 19.2 | 38.0 | 47.3 | 29.1 | 52.5 | 63.2 | FSSD512 [43] | VGG16 | 31.8 | 52.8 | 33.5 | 14.2 | 35.1 | 45.0 | 22.3 | 49.9 | 62.0 | RFB512 [44] | VGG16 | 34.4 | 55.7 | 36.4 | 17.6 | 37.0 | 47.6 | 27.3 | 52.3 | 65.4 | MPH-SSD | VGG16 | 51.1 | 79.8 | 55.6 | 21.1 | 36.3 | 59.4 | 28.4 | 46.8 | 67.9 |
|
|