Name
Title | ||
|---|---|---|
Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection |
Abstract | ||
|---|---|---|
The development of object detection technology makes it possible for robots to interact with people and the environment, but the changeable application scenarios make the detection accuracy of small and medium objects in the practical application of object detection technology low. In this paper, based on multi-scale feature fusion of indoor small target detection method, using the device to collect different indoor images with angle, light, and shade conditions, and use the image enhancement technology to set up and amplify a date set, with indoor scenarios and the SSD algorithm in target detection layer and its adjacent features fusion. The Faster R-CNN, YOLOv5, SSD, and SSD target detection models based on multi-scale feature fusion were trained on an indoor scene data set based on transfer learning. The experimental results show that multi-scale feature fusion can improve the detection accuracy of all kinds of objects, especially for objects with a relatively small scale. In addition, although the detection speed of the improved SSD algorithm decreases, it is faster than the Faster R-CNN, which better achieves the balance between target detection accuracy and speed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3389/fnbot.2022.881021 | FRONTIERS IN NEUROROBOTICS |
Keywords | DocType | Volume |
indoor scene, small target detection, convolutional neural network, multi-scale feature fusion, SSD | Journal | 16 |
ISSN | Citations | PageRank |
1662-5218 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Huang | 1 | 0 | 0.34 |
| Cheng Chen | 2 | 0 | 0.34 |
| Juntong Yun | 3 | 4 | 4.44 |
| Ying Sun | 4 | 0 | 0.34 |
| Jinrong Tian | 5 | 1 | 1.02 |
| Zhiqiang Hao | 6 | 0 | 1.35 |
| Hui Yu | 7 | 0 | 0.34 |
| Hongjie Ma | 8 | 0 | 0.34 |